Method and system for creating a predictive model for targeting web-page to a surfer

ABSTRACT

A system and a method for creating a predictive model to select an object from a group of objects that can be associated with a requested web-page, wherein a configuration of the requested web-page defines a subgroup of one or more selected objects from the group of objects. Each web-page can include one or more links to be associated with content objects from the group. For each content object presented over a requested web-page, one or more predictive model with relevant predictive factors is processed such that the predicted objective, the probability of success for example, is calculated. A success is defined as a surfer responding to the presented content according to the preferences of the site owner. Each predicted model can be associated with a key-performance indicator (KPI). Further, a predictive model can reflect the number of times the surfer requested the web page during the surfer&#39;s visit.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is being filed under 35 USC 111 and 37 CFR 1.53(b) as acontinuation-in-part application of the patent application filed in theUnited States under the title of METHOD AND SYSTEM FOR CREATING APREDICTIVE MODEL FOR TARGETING CONTENT TO A SURFER on Jul. 16, 2009 andassigned Ser. No. 12/504,265, which application claims the benefit ofthe filing date of the provisional application for patent filed in theUnited States under the title of METHOD AND SYSTEM FOR PROVIDINGTARGETED CONTENT TO A SURFER on Jul. 25, 2008 and assigned Ser. No.61/083,551 and the provisional application for patent filed in theUnited States under the title of METHOD AND SYSTEM FOR CREATING APREDICTIVE MODEL FOR TARGETING CONTENT TO A SURFER on Jul. 25, 2008 andassigned Ser. No. 61/083,558. The present application is related to thepatent application that was filed in the United States under the titleof METHOD AND SYSTEM FOR PROVIDING TARGETED CONTENT TO A SURFER on Jul.16, 2009 and assigned Ser. No. 12/503,925. Each of these patentapplications and provisional applications for patent are hereinincorporated by reference in their entirety.

BACKGROUND

More and more people are communicating via the Internet and othernetworks. The Internet, in particular, is a hierarchy of many smallercomputer networks, all of which are interconnected by various types ofserver computers. Some of the servers interconnected through theInternet provide database housing as well as storage of a plurality ofweb-pages, generally referred to as the World Wide Web (WWW). By virtueof being accessible through the WWW, these web-pages may be retrieved byrandom Internet users, i.e. surfers, operating computers with a browser.Exemplary computers can be a personal computer (PC), a laptop, anotebook computer, a cellular telephone, a handheld computer, a personaldata assistant (PDA), or any other computing device with wired orwireless communication capabilities communicable over an IP network.

Some common examples of browser applications used by Internet surfersare Openwave Systems Inc. or Opera Mobile Browser (a trademark of OperaSoftware ASA), Microsoft Internet Explorer (a trademark of Microsoft),and Firefox Web Browser. Using a web browser application on a computerthat is connected to the Internet, surfers may retrieve web-pages thatinclude information such as news, professional information,advertisements, e-commerce links, content downloads, etc. A commonbrowser application may use Hyper Text Transport Protocol (HTTP) inorder to request a web-page from a website. Upon receipt of a web-pagerequest by a browser, the website responds by transmitting a markuplanguage file, such as Hypertext Markup Language (HTML), that isrepresentative of the requested page or pages. Notably, HTTP requestsand HTML responses are well known in the art and are used asrepresentative terms for the respective markup language files throughoutthis disclosure.

A common web-page may include numerous buttons, or links, operable toredirect a surfer to other locations within the website or on theInternet. These links offer a surfer a path to a next event which may bethe presentation of another web-page, embedded content within thepresent web-page (e.g. an image, an advertisement, a banner, etc.), aplayable media file, a number for forwarding a short message service(SMS) with a password, an application form, a registration form, etc.

A common link design may display a name of a category of information,such as news, sport, or economics. Other link designs may comprise abanner or an image intended to attract the attention of a surfer to anadvertisement, an image, a text message that prompts the surfer to diala number and use a password, etc. If a surfer is enticed to explore theoffer shown on the link design, the surfer will use a pointing devicesuch as a mouse, and place the pointer over the selected button, whichmay be comprised of a banner for example, and issue a command by“clicking” the mouse to “click through” on that button. In such ascenario, the surfer's browser may return information from a websiteassociated with the particular banner that comprised the link.

In the present description, the terms “selecting button,” “selectionbutton,” “redirecting button,” “slot”, “link,” “Hyper Link” and “banner”are used interchangeably. The terms “banner” and “slot” can be used as arepresentative term for the above group. An “advertisement” (AD) or“object” may be used as representative terms for content. Exemplarytypes of content can be the text of an AD as well as an AD's font,color, design of the object, an image, the design of the page in whichthe object is presented, etc.

The benefit from presenting a web-page, as well as improving a surfer'sexperience when surfing the web-page, can be increased if the selectionbuttons within the web-page are targeted toward the immediate observer.There are numerous existing methods and systems for offering targetedcontent in a web based environment. Some of the methods employquestionnaires containing categorized questions on user preferences.Such methods require the management of huge databases containinginformation on a large number of users. Besides the cumbersomemanagement of all the information acquired from questionnaires, anothernegative is that many users prefer not to reply to a personalizequestionnaire.

Other methods for identifying and offering targeted content in a webenvironment make use of client applications installed on a user'scomputer. The client applications are operable to track a user'sactivity on the web and subsequently report a compilation of the trackedactivity to an associated web server or content server. Such methods arenot popular with many users concerned with privacy. Further, suchmethods may require the often costly and awkward installation of aclient application on a user's computer.

Some methodologies for delivery of targeted content may comprise alearning period and an ongoing period. During the learning period, aplurality of options of content within a certain web-page may bepresented to various surfers. The response of the surfers to the variouscontent options is monitored throughout the learning period inanticipation of ultimately employing the best performing alternative.During the subsequent ongoing period, all surfers requesting the certainweb-page will be exposed to the previously selected alternative. Such analgorithm is often referred to as “The king of the Hill” algorithm.While a “King of the Hill” approach can fit the preference of a largegroup of surfers, it is prone to missing other groups of surfers thatprefer other content delivery alternatives.

BRIEF SUMMARY

There is a need in the art for a method and system that can calculatepredictive models for determining an object to be presented inassociation with a given link in a web-page used to attract an observerand entice a response. Exemplary responses may be the selection, orclicking of, a certain link (banner, for example), sending of an SMS,calling a number and using a password, the amount of revenue that wascreated by selecting this link, the number of visits of a surfer in acertain page, which were needed until the revenue was created,converting rate, etc. In an embodiment of the present disclosure, whencalculating the predictive model the expected value to the contentprovider is taken into account. In order to save expenses andcomplexity, it is desired that the system be transparent to a surfer andwill not require a database for storing information associated with anumber of surfers. Converting is the state in which a surfer converts alink according to the desire of the website owner. Purchasing is anexample for converting, filling a questionnaire can be another type ofconverting, registration, is another type of converting and so on.

Exemplary embodiments of the present disclosure seek to provide novelsolutions for determining which content object taken from a group ofcontent objects will be best suited for presentation in association witha link on a requested web-page. Exemplary types of content objects maycomprise the text, topic, font, color, image, or other attribute of anadvertisement. Still other content objects may comprise the specificdesign of the object, an image, the design of the page in which theobject is presented, etc. Other embodiments may determine how to respondto a surfer request from the website. The decision process for selectinga content object can be based on information that is associated with therequest, such as a common HTTP request. Exemplary associated informationmay include the day and time of receipt of the request for the web-page,the IP address and/or domain from which the request was sent, the typeand the version of the browser application that is used for requestingthe web-page, and the URL used by a surfer for requesting the web-pagewith the parameters that are attached to the URL.

Other types of information that is associated with the request can bestatistical information indicative of the behavior of a surfer inrelation to the website to which the request was sent. Behavioralinformation may include timers, frequency of visits by a surfer to thewebsite, the last time that a surfer requested a web-page, etc. Otherbehavioral information may include one or more counters wherein eachcounter can count the number of events of a certain type. Exemplarycounters can measure the number of visits by a specific surfer to therelevant website, the number of requests for a certain web-page withinthe website, the number of times a certain offer (content object) wasselected or not selected, etc. Even further, some of the exemplarycounters may be time dependent such that the value of the counterdescends over time. In some exemplary embodiments of the presentdisclosure, the behavioral information may be embedded within a cookiethat is related to the relevant website or a third party cookie.

Still other exemplary types of information comprise grouping orcategorizing information. Grouping information can be delivered by a webserver that contains a requested web-page. The grouping information canbe associated with a group to which a current surfer belongs, surfer'sgrouping information (SGI). Exemplary surfer's grouping information canbe used to define attributes of the group such as preferred sportclothing, preferred brand names, marital status, gender, age, etc.Surfer's grouping information can be managed by the web server and/or bya 3rd party server, and added to a field in a cookie associated with acertain surfer, for example.

Other grouping information can reflect attributes of the content,content grouping information (CGI). The CGI can be related to therequested web-page as well as attributes of the content objectspresented in the requested web-page. Exemplary CGI can define attributesof the page or the content object such as the cost of a product, aproduct brand, vacation information, sport information, etc. CGI can bemanaged by the web server and added to a field in the URL of a certainweb-page or URL of a certain content object, for example.

Each type of information, associated information, statisticalinformation and grouping information can be retrieved and processed fordefining one or more predictive factors which can be used in one or morepredictive models. The predictive factors are used for calculating thepredictive value gained by the website when presenting each contentelement. This value can also be the probability of a certain contentobject, from a group, to be selected, to be clicked, by a surfercurrently observing the requested web-page. The predictive factors canbe referred to as extracted features.

Some embodiments use a utility value or a key-performance indicator(KPI). A utility value, or the KPI value, can represent a website'sbenefit when the alternative is explored by a surfer. The benefit can bethe rate of clicking (selecting) on a certain object in the presentedweb-page, the rate of converting a certain object, purchasing rate, therevenue rate which was created by presenting the web-page, etc. Theprobability can be multiplied by the associated utility to obtain theexpected utility when presenting the alternative. In the presentdescription, the terms “predictive factor,” and “predictive variable”may be used interchangeably.

In some embodiments the predictive model of a certain web-page takesinto consideration the visit of a surfer in the website and the numberof times the same web-page has been requested until creating a utilityvalue, a certain KPI, related to the web-page. A visit of a surferstarted from the first request to the website and ended after a certainperiod of the surfer's inactivity with the website. The period can befew tens of minutes of inactivity, 30 minutes for example. For apredictive model of a web-page that reflects the visit of the surfer inthe website, the weigh of the utility value or the KPI, which wascreated at the end of the visit, can be divided by the number of timesthe surfer observed the relevant web-page until determine to select, orpurchase certain goods or services or create a certain revenue to thewebsite owners.

An exemplary embodiment may build in parallel two or more predictivemodels for the same web-page. Each model can be related to a differentKPI. Each model can be ready to be used in a different time. Building apredictive model per each KPI requires a certain level of variancewithin each KPI samples. In general the number of events in which theKPI is just clicking, fetching a certain object, is more frequent thanthe number of events in which a link is converted into a purchase or todeliver certain revenue. Therefore the required level of variance in theclicking samples can be reached in a shorter learning period than thelearning period which is needed for preparing a predictive model forpurchasing KPI. Consequently, the predictive model that is related toKPI which is just click on a link will be ready before a predictivemodel that is related to revenue KPI.

An exemplary embodiment that creates two or more predictive models perweb-page, wherein each model is related to another KPI, can be able todetermine when to switch from one predictive model to the other. Thedecision can be based on the readiness of the models and the utility ofthe relevant KPI to the web-page owners. For example, a predictive modelthat is related to revenue can be the last to be ready; however it canbe the most important one to the website owner. Therefore, such anembodiment may start providing targeted content based on a predictivemodel that is related to “click” KPI and later on it may automaticallyswitch to use a predictive model that is related to converting rate KPIand finally may use predictive model that maximize the revenue KPI, forexample.

Revenue KPI can be measured as the amount of dollars, Euros, or othercurrency which was created from a certain web-page. In some embodimentsthe revenue KPI can be measured as the amount of dollars, Euros, orother currency which was created during a certain visit, etc.

An exemplary embodiment of the present disclosure can create a bank ofpredictive models. Each predictive model can be associated with acontent object from the set of content objects that can be presentedover the requested web-page. An exemplary predictive model may includeone or more predictive factors with each predictive factor (predictivevariable) being associated with a coefficient in a predictive formula.Exemplary predictive formulas can be based on known predictivealgorithms such as, but not limited to, logistic regression, linearregression, decision tree analysis, etc. Some exemplary embodiments ofthe present disclosure can use linear or logistic regression, with orwithout stepwise methods, while calculating the predictive formula.

A predictive factor can also be a subset of values of certain variablessuch as, for example, the weekdays Monday and Saturday. The coefficientcan thus outline the effect a predictive factor has on the predictedprobability that a relevant content object will trigger a desiredresponse from a surfer. The desire response, which is the KPI, can bethe clicking, converting, creating revenue, etc. Exemplary predictivefactors may include, for example, the day in the week (Monday andSaturday, for example), the hour, the browser type, the number of visitsto the site, the content object that is presented in accordance withanother link on the same web-page, the total elapsed time from the lastvisit, etc.

Exemplary predictive models can include some constants that are relatedto the content object associated with the model. Exemplary constants maybe a utility constant which reflects the benefits the owner of thewebsite receives when the relevant content object is selected or anarithmetic factor.

For each content object presented over a requested web-page, apredictive model with relevant predictive factors is processed such thatthe predicted objective, the probability of success for example, iscalculated. A success is defined as a surfer responding positively tothe presented content. For example, should a surfer select a relevantcontent object, the probability of the objects that can be presented iscalculated. Subsequently, the combination of one or more objects withthe highest predictive expected utility are selected to be associatedwith the links in the web-page requested by the surfer. The markup filethat represents the web-page is modified such that its links point tothe selected objects. The predictive objective value can be calculatedto correspond to the predictive model. For example, in the logisticregression predictive model, the optimal linear predictive function iscalculated and converted using a link function such as “Log Odds,”

An exemplary embodiment of the present disclosure may include a learningmodule. An exemplary learning module may be adapted to monitor the dataexchange between a plurality of random surfers at one or more websites.Further, it can collect information on objects embedded and offeredwithin the requested web-pages as well as track how each of the randomsurfers responds to those offers. From time to time, the exemplarylearning module can process the sampled data in order to determine whichpredictive factors are relevant for each one of the objects andcalculated an associated coefficient for success. Per each object, itsassociated statistical module can be updated or recalculated using thenew or updated predictive factors.

An exemplary embodiment of the present disclosure can operate in eitherof two modes of operation, i.e. learning mode and ongoing mode. Thelearning mode can be executed after the initialization or when thecontent of the website is changed, for example. During the learningmode, new predictive models are calculated. The ongoing mode can beexecuted after the learning mode and may monitor and tune the existingpredictive models.

During the learning mode, a large portion of communication sessions aresampled in order to define the new predictive models. During the ongoingmode, the size of the sample can be reduced and the predictive model canbe tuned. When a significant change in the performance of a predictivemodel is observed, then a notification can be issued and/or a newpredictive model can be created.

Some embodiments, during the learning mode, may collect information thatis related to a visit. The information may include the number of timesthat the relevant webpage was retrieved by a certain surfer during acertain visit, etc. There are embodiments that during the learning modecollect information that related to different KPI. Such embodiments maydeliver two or more predictive models per web-page. Each predictivemodel can be associated with different KPI and each predictive model canbe ready in different time. Such embodiments are capable of switchingbetween predictive models based on their readiness and their utility tothe website owner.

Unless otherwise defined, all technical and/or scientific terms usedherein have the same meaning as commonly understood by one of ordinaryskill in the art to which the invention pertains. Although methods andmaterials similar or equivalent to those described herein can be used inthe practice or testing of embodiments of the invention, exemplarymethods and/or materials are described below. In case of conflict, thepatent specification, including definitions, will control. In addition,the materials, methods, and examples are illustrative only and are notintended to be necessarily limiting. Moreover, the language used in thisdisclosure has been principally selected for readability andinstructional purposes, and may not have been selected to delineate orcircumscribe the inventive subject matter, resort to the claims beingnecessary to determine such inventive subject matter. Reference in thespecification to “one embodiment” or to “an embodiment” means that aparticular feature, structure, or characteristic described in connectionwith the embodiments is included in at least one embodiment of theinvention, and multiple references to “one embodiment” or “anembodiment” should not be understood as necessarily all referring to thesame embodiment.

Implementation of the method and/or system of embodiments of theinvention can involve performing or completing selected tasks manually,automatically, or a combination thereof. Moreover, according to actualinstrumentation and equipment of embodiments of the method and/or systemof the invention, several selected tasks could be implemented byhardware, by software or by firmware or by a combination thereof usingan operating system. Software may be embodied on a computer readablemedium such as a read/write hard disc, CDROM, Flash memory, ROM, etc. Inorder to execute a certain task, a software program may be loaded to anappropriate processor as needed.

The foregoing summary is not intended to summarize each potentialembodiment or every aspect of the present disclosure. Other features andadvantages of the present disclosure will become apparent upon readingthe following detailed description of the embodiments with theaccompanying drawings and appended claims.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING

Exemplary embodiments of the present disclosure will be understood andappreciated more fully from the following detailed description, taken inconjunction with the drawings in which:

FIG. 1 is a simplified block diagram illustration of an exemplaryportion of a communication network in which exemplary embodiments of thepresent disclosure can be used.

FIG. 2 a schematically illustrates a simplified block diagram withrelevant elements of an exemplary content adapting server (CAS) thatoperates according to an exemplary technique of the present disclosure.

FIG. 2 b schematically illustrates a simplified block diagram withrelevant elements of another exemplary content adapting server (CAS)that operates according to another exemplary technique of the presentdisclosure.

FIGS. 3A & 3B schematically illustrate a flowchart showing relevantprocesses of an exemplary embodiment of a management method.

FIG. 4 schematically illustrates a flowchart showing relevant processesof an exemplary embodiment of a method for handling a request for aweb-page.

FIG. 5 schematically illustrates a flowchart showing relevant processesof an exemplary embodiment of an observation manager module.

FIG. 6 schematically illustrates a flowchart showing relevant processesof an exemplary embodiment of a method for handling a ML file of arequested web-page.

FIG. 7 schematically illustrates a flowchart showing relevant processesof an exemplary embodiment of a method for selecting an optional objectto be presented on a requested web-page.

FIG. 8 schematically illustrates a flowchart showing relevant processesof an exemplary embodiment of a method for monitoring the predictivemodels.

FIGS. 9A & 9B schematically illustrate a flowchart showing relevantprocesses of an exemplary embodiment of a method for building apredictive model.

DETAILED DESCRIPTION

In the following description, for purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the invention. It will be apparent, however, to oneskilled in the art that the invention may be practiced without thesespecific details. In other instances, structure and devices are shown inblock diagram form in order to avoid obscuring the invention. Referencesto numbers without subscripts or suffixes are understood to referenceall instance of subscripts and suffixes corresponding to the referencednumber. Moreover, the language used in this disclosure has beenprincipally selected for readability and instructional purposes, and maynot have been selected to delineate or circumscribe the inventivesubject matter, resort to the claims being necessary to determine suchinventive subject matter. Reference in the specification to “oneembodiment” or to “an embodiment” means that a particular feature,structure, or characteristic described in connection with theembodiments is included in at least one embodiment of the invention, andmultiple references to “one embodiment” or “an embodiment” should not beunderstood as necessarily all referring to the same embodiment.

Although some of the following description is written in terms thatrelate to software or firmware, embodiments may implement the featuresand functionality described herein in software, firmware, or hardware asdesired, including any combination of software, firmware, and hardware.In the following description, the words “unit,” “element,” “module” and“logical module” may be used interchangeably. Anything designated as aunit or module may be a stand-alone unit or a specialized or integratedmodule. A unit or a module may be modular or have modular aspectsallowing it to be easily removed and replaced with another similar unitor module. Each unit or module may be any one of, or any combination of,software, hardware, and/or firmware, ultimately resulting in one or moreprocessors programmed to execute the functionality ascribed to the unitor module. Additionally, multiple modules of the same or different typesmay be implemented by a single processor. Software of a logical modulemay be embodied on a computer readable medium such as a read/write harddisc, CDROM, Flash memory, ROM, or other memory or storage, etc. Inorder to execute a certain task a software program may be loaded to anappropriate processor as needed.

In the description and claims of the present disclosure, “comprise,”“include,” “have,” and conjugates thereof are used to indicate that theobject or objects of the verb are not necessarily a complete listing ofmembers, components, elements, or parts of the subject or subjects ofthe verb.

The present disclosure relates to the presentation of data overcommunication networks, such as the Internet, and more particularly toselecting one or more objects (content of a web link) from a group ofobjects of content, to be presented over a requested web-page. An“object” may be used as representative terms for content. Exemplarytypes of content can be the text of an AD as well as an AD's font,color, design of the object, an image, the design of the page in whichthe object is presented, etc.

Turning now to the figures in which like numerals represent likeelements throughout the several views, exemplary embodiments of thepresent disclosure are described. For convenience, only some elements ofthe same group may be labeled with numerals. The purpose of the drawingsis to describe exemplary embodiments. Therefore, features shown in thefigures are chosen for convenience and clarity of presentation only.

FIG. 1 depicts a block diagram with relevant elements of an exemplarycommunication system 100, which is a suitable environment forimplementing exemplary embodiments of the present disclosure.Communication system 100 may comprise a plurality of surfer terminals(ST) 110; a network 120 such as, but not limited to the Internet, theworld wide web (WWW); one or more personalize content servers PCS 140;and one or more personalize content domains (PCD) 150.

The communications system 100 may be based on the Internet Protocol(IP). Network 120 may represent one or more network segments including,but not limited to, one or more Internet segments, one or moreIntranets, etc. Network 120 may run over one or more types of physicalnetworks such as, but not limited to, Public Switched Telephone Network(PSTN), Integrated Services Digital Network (ISDN), cellular networks,satellite networks, etc. Further, network 120 may run over a combinationof network types. Network 120 may also include intermediate nodes alongthe connection between a surfer and a content server. The intermediatenodes may include, but are not limited to, IP service provider servers,cellular service provider servers and other types of network equipment.

It will be appreciated by those skilled in the art that depending uponits configuration and the needs, communication system 100 may comprisemore than three ST 110, three PCS 140 and three PCD 150. However, forpurposes of simplicity of presentation, three units of each are depictedin the figures. Further, it should be noted that the terms: “surferterminal”, “terminals,” “endpoint computer,” “endpoint,” “surfer,”“user's device,” “mobile phone” and “user” may be used interchangeablyherein.

A plurality of ST 110 may be served by system 100 for surfing theInternet 120 and fetching web-pages from the one or more PCS 140 or PCD150. Exemplary ST 110 can be a personal computer (PC), a laptop, anotebook computer, a cellular telephone, a handheld computer, a personaldata assistant (PDA), or any other computing device with wired orwireless communication capabilities communicable over an IP network. Acommon ST 110 may run a browser application such as, but not limited to,Openwave Systems Inc. or Opera Mobile Browser (a trademark of OperaSoftware ASA), Microsoft Internet Explorer (a trademark of Microsoft),or Firefox Web Browser, etc. The browser application can be used forrendering web-pages. Exemplary web-pages may include information such asnews, professional information, advertisements, e-commerce content, etc.

A common browser application can use HTTP while requesting a web-pagefrom a website. The website can respond with a markup language file suchas but not limited to HTML files. Herein the term HTML is used as arepresentative term for a markup language file. HTTP requests and HTMLresponses are well known in the art.

Exemplary PCS 140 and/or PCD 150 may receive HTTP requests from theplurality of ST 110 and deliver web-pages in the form of markup languagefiles such as, but not limited to, HTML files. An exemplary PCS 140 maycomprise a surfer's interface unit (SIU) 143 and a content adaptingmodule (CAM) 146. Exemplary SIU 143 can execute common activities of acontent server. Further, it may receive HTTP requests and respond withHTML files. In addition, exemplary SIU 143 may communicate with CAM 146and/or deliver information about the activities of the different ST 110.The activity can be the web-pages requested by the surfers, for example.

In some exemplary embodiments of the present disclosure, SIU 143 maydeliver, to CAM 146, information about surfer's attributes. Exemplaryattributes may be a surfer's purchasing habits (expensive,not-expensive, brand name, etc.). A specific surfer's information can bemanaged by the web server and be added to a field in the cookieassociated with the particular surfer.

In some embodiments, the information can also be related to therequested web-page as well as particular attributes of the contentobjects to be presented in the requested web-page. Exemplary informationthat may define attributes of the web-page or the content objectinclude, but are not limited to, the cost of a product, the brand of aproduct, vacation information, etc. This type of information can bemanaged by the web server and added to a field in the URL of a certainweb-page or URL of a certain content object, for example.

CAM 146 may process requests of different surfers in order to create aplurality of predictive modules, wherein each predictive module can beassociated with an optional object that is presented over a requestedweb-page in one of the slots (redirection-button) from a set of optionalslots. The predictive modules can be used to select a set of one or moreobjects that maximizes an expected benefit to the site owner. In thepresent description, the terms “expected benefit” and “expected utility”can be used interchangeably. The sets of slots, in which an optionalobject can be presented, and the selected optional object to bepresented in each slot (Slot/Optional-object) can be defined within theconfiguration of the web-page. The configuration of theobject/redirection-buttons with the optimal prediction to be selectedfor the requesting surfer is transferred to SIU 143. In response, SIU143 may modify the HTML file to include those objects in the relevantlinks.

Some embodiments of CAM 146 may handle two or more predictive models perweb page. Each predictive model can be associated with different KPI andeach predictive model can be ready in different time. Such embodimentsare capable of switching between predictive models based on theirreadiness and their utility to the website owner. In some embodiments, apredictive model can target a weighted mix of KPIs.

There are some embodiments of CAM 146 that may create predictive modelper web-page taking into consideration the visit of a surfer. In suchembodiment the weight of the KPI, which was achieved at the end of thevisit and is related to the relevant web-page, can be divided by thenumber of times the relevant web-page had been requested and rendered tothe surfer during the visit until a decision was made by the surfer.

Exemplary PCD 150 can include a plurality of common content servers 152and one or more content adapting servers (CAS) 154. CAS 154 can beconnected as an intermediate node between the plurality of ST 110 andthe content servers 152. In one exemplary embodiment, CAS 154 may beconfigured as the default gateway of the PCD 150. In another exemplaryembodiment, CAS 154 may physically reside between the PCD 150 and thenetwork 120.

In yet another exemplary embodiment of the present disclosure, aredirector may be included and CAS 154 can be configured as atransparent proxy. In such an embodiment, CAS 154 may be transparent toboth sides of the connection, to ST 110 and to the content servers 152.In an alternate exemplary embodiment of the present disclosure CAS 154may be used as a non-transparent proxy. In such an embodiment, the ST110 can be configured to access CAS 154 as their proxy, for example. Inan alternate exemplary embodiment of CAS 154, not shown in the drawing,can communicate with a website by using a certain application programinterface (API). Yet, in an alternate exemplary embodiment of CAS 154,not shown in the drawing, can be invoked by a link to a JavaScript (JS).More information on those embodiments is disclosed below.

CAS 154 can intercept the data traffic between the plurality of ST 110and content servers 152. CAS 154 tracks the behavior of a plurality ofsurfers in order to create predictive models for content objects to beassociated with requested web-pages. Subsequently, when operating in anongoing mode, the predictive models can be used for determining whichobjects to assign to a web-page requested by a given surfer.

Toward PCD 150, CAS 154 can process requests of surfers for web-pagesfrom one or more of the content servers 152, for retrieving a pluralityof predictive factors to be used in a plurality of predictive modules,similar to the predictive modules that are used by exemplary CAM 146. Inthe other direction, CAS 154 can process the responses (the HTML files,for example) and determine, based on the predictive models, which objectto present. The HTML file, which represents the web-page, is modified toinclude those objects in the links. More information on the operation ofPCS 140 and CAS 154 is disclosed below in conjunction with FIGS. 2 to 9a and b.

The following paragraphs are related to FIG. 2 a and FIG. 2 b. FIG. 2 a,each illustrates a block diagram with relevant elements of an exemplaryembodiment of a content adapting server (CAS) 200 or 2000 that operatesaccording to certain techniques of the present disclosure. Exemplary CAS200 or 2000 can be installed in association with PCD 150, as unit 154(FIG. 1) for example.

In one exemplary embodiment, CAS 200 may intercept the communicationbetween the plurality of ST 110 and the plurality of content servers 152(FIG. 1). CAS 200 can process surfer requests in order to track andlearn about the behavior of each surfer. Based on learned informationobtained from a plurality of surfers, CAS 200 can create a plurality ofpredictive models that correspond to given web-pages. Each predictivemodel can be assigned to an object that is associated with a redirectingbutton in a requested web-page. The exemplary CAS 200 creates thepredictive models according to a single KPI.

FIG. 2 b illustrates a block diagram with relevant elements of anexemplary embodiment of a content adapting server (CAS) 2000 thatoperates according to certain techniques of the present disclosure. Theexemplary CAS 2000 creates two or more type of predictive models pereach optionally objects in the relevant web-page. Each type ofpredictive model is associated with another type of KPI. One type of KPIcan be clicking rate, other type of KPI can be conversion rate, etc. Inorder to eliminate redundancy of information, in the followingparagraphs elements of both embodiments, CAS 200 and CAS 2000, which aresimilar, are marked with similar numeral and are disclosed once, inrelation with FIG. 2 a and CAS 200. Base on reading the description ofCAS 200, an ordinary skilled person in the art is expected to understandhow those similar elements operate in relation with CAS 2000.

After creating the predictive models, CAS 200 can use the models inorder to determine which combination of optional objects can beassociated with certain redirecting buttons in a requested web-page suchthat the profit of the owner of the PCD 150 is maximized. Thiscombination of optional objects is referred to as a predictiveconfiguration of the web-page. Markup language files, such as HTMLfiles, are transferred from content servers 152 toward ST 110 and can bemodified by CAS 200, for example, according to the predictedconfiguration. The profit of the owner can be measured by KPI parametersuch as but not limited to: the clicking rate on an object of adelivered web-page according to the predictive configuration; theconverting rate, the rate of converting a link to a deal from the totalnumber of events of presenting the page based on the predictiveconfiguration; the revenue rate, the average revenue that is created bypresenting the predictive configuration of a certain webpage compare toother configuration of the same web-page, etc. CAS 2000 of FIG. 2 b canprocess two or more type of predictive models simultaneously. Each typecan be associated with another type of KPI or a combination of one ormore KPIs.

The behavior of surfers can be monitored and learned during the ongoingoperation of an exemplary CAS 200. The monitored data can be used inorder to improve the predictive models.

An exemplary CAS 200 can be divided into two sections, a networkinterface section and a content adaptation section. The networkinterface section may comprise an HTTP proxy 210, a page request module(PRM) 220, an active surfer table (AST) 215, or 2150 for the example ofCAS 2000, and a markup language file handler (MLFH) 230. The contentadaptation section may include one or more page data collectors (PDC)240, one or more page object selection modules (POSM) 250, and amanagement and prediction module (MPM) 260. CAS 2000 may include a PDC2400 instead of PDC 240. PDC 2400 act in a similar way to PDC 240,however the collected data per web-page is organized according to thetypes of KPI. Further, CAS 2000 may include an AST 2150 instead of AST215. Along the disclosure the term AST 215 may refer also to AST 2150unless for certain cases it is written differently and the term PDC 240may refer also to PDC 2400 unless for certain cases it is writtendifferently. Data communication between the internal modules of CAS 200may be implemented by using components such as, but not limited to,shared memory, buses, switches, and other components commonly known inthe art that are not necessarily depicted in the drawings.

In an alternate exemplary embodiment of CAS 200 or 2000, not shown inthe drawing, CAS 200 or 2000 can communicate with a website by using acertain application program interface (API). In such embodiment thesurfer requests as well as the responded web pages are sent directlybetween the surfer and the website. The website, upon receiving arequest parses the request, retrieved from the request information whichis required by CAS 200 or 2000. Information such as the URL, thereceiving time, the cookie, the browser type, etc. This information issent from the website toward CAS 200 or 2000 by using the API. Then thewebsite waits to get the instructions about the recommendedconfiguration from CAS 200 or 2000. Upon receiving the information aboutthe request, CAS 200 or 2000, process it, update the cookie, encode thecookie, determine which configuration is recommended and sent this data,by using the API to the website. The website in its turn, processes thereceived information from CAS 200 or 2000, defines the configuration ofthe webpage, and sends the webpage toward the requesting server.

Thus, such an exemplary embodiment of CAS 200 or 2000 may not need theHTTP proxy 210. Further, the PRM 220 and the MLFH 230 can be slightlymodified to communicate with the website based on the API, processed thereceived information and responding to the website over the API.

Yet, in an alternate exemplary embodiment of CAS 200 or 2000, not shownin the drawing, CAS 200 or 2000 can be invoked by a link to a JavaScript(JS). In such embodiment the surfer requests as well as the respondedweb pages are sent directly between the surfer and the web-page. Howeverthe responded website includes one or more links. Each link requests aJS. The first link on the top of the webpage is a link to CAS 200 or2000. In some embodiments the first link can point on a Cache DomainNetwork (CDN) server. The CDN server in its turn can transfer therequest to the first JS to the CAS 200 or 2000. CAS 200 or 2000 canrespond by sending a JS code that when it is activated by the browser atthe surfer computer, is adapted to collect information that is requiredby the CAS 200 or 2000. Information such as the URL, the receiving time,the cookie, the browser type, etc. This information is sent by thebrowser running the JS from the surfer toward the CAS 200 or 2000. Uponreceiving the information about the request, CAS 200 or 2000, processesit, determines a configuration of the requested web page, update thecookie, encode the cookie and send it to the JS. Updated the cookie canbe done by adding information about the configuration that was sent tothe user. In addition CAS 200 or 2000 determines which configuration tosend to this surfer. In parallel the browser at the surfer continueparsing the web page and reaches the next link to a next JS, the browsersend a request to the next JS, in response the CAS 200 or 2000 respondswith the appropriate object to be presented.

Thus, the alternate exemplary embodiment of CAS 200 or 2000 the HTTPproxy 210, the PRM 220 and the MLFH 230 can be slightly modified tocommunicate with the surfer and process the requested JS.

An exemplary PCS 140 (FIG. 1), in addition to common elements of atypical web server, may comprise elements which are similar to theelements of CAS 200. For example, SIU 143 (FIG. 1) may comprise elementshaving functionality similar to an HTTP proxy 210, a PRM 220, an AST 215and an MLFH 230. An exemplary CAM 146 (FIG. 1) may comprise elementsthat are similar to those of a PDC 240 or PDC 2400, a POSM 250, and anMPM 260. Therefore, the operation of an exemplary PCS 140 may be learnedby one skilled in the art from the detailed description of a CAS 154and, to avoid unnecessary redundancy, will not be specifically describedherein.

In exemplary embodiments of the present disclosure, in which thecommunication over network 120 is based on IP, HTTP proxy 210 may beadapted to handle the first layers of the OSI (open systeminterconnection) seven layer communication stack. The layers may be thePhysical Layer, Data Link Layer, transport Layer (the TCP stack), andthe Network Layer. In exemplary embodiments of the present disclosure,in which the CAS 200 is transparent to the ST 110 (FIG. 1) as well as tothe content servers 152 (FIG. 1), the HTTP proxy 210 may behave as atransparent proxy and may use a redirector. The transparent HTTP proxy210 may be adapted to obtain packets traveling from/to the plurality ofST 110 (FIG. 1) and to/from the plurality of the content servers 152 ofthe domain 150. The header of the packets may be processed in order todetermine how to route the received packets. HTTP requests for web-pagesmay be routed toward PRM 220 and responses that include web-pages in theform of a markup language file may be routed toward MLFH 230. Otherpacket types may be transferred toward their destination in an “as is”form.

Data coming from the internal module of CAS 200 is transferred via HTTPproxy 210 toward their destination. For example, HTTP requests forweb-pages, after being processed by PRM 220, are transferred toward theappropriate content server 152 (FIG. 1). HTML files, after being handledby MLFH 230, are transferred toward the appropriate ST 110 via HTTPproxy 210 to network 120.

Exemplary PRM 220 may comprise a bank of domain counters 222, a cookiedecompression and update module (CDUM) 224 and a timer 226. PRM 220 mayreceive, from HTTP proxy 210, requests for web-pages that are targetedtoward content servers 152 (FIG. 1). The requests are processed in orderto collect information that may be used for preparing predictive models,monitoring the predictive models, or retrieving predictive factors to beplaced in a predictive model that is used for defining the configurationof the requested web-page. After processing the request, the processedrequest is transferred toward the content servers 152 via HTTP proxy210. The collected information may be written in an entry of AST 215that is associated with the requester of the web-page.

The collected information may include associated information such as,but not limited to, the day and the time the request for the web-pagewas received, the IP address or/and IP port from which the request wassent, the type and the version of the browser application used forrequesting the web-page, and the URL used by a surfer for requesting theweb-page with the parameters that are attached to the URL.

In addition, the retrieved information may include behavioralinformation. The behavioral information may be statistical informationthat is managed by CAS 200. It may be divided into a requester's relatedbehavioral information and a domain's related information. Therequester's related behavioral information may include timers indicatingone or more previous visits by the requester to the domain PCD 150 (FIG.1), the last time that the requester requested a certain web-page, thenumber of the requester's visits in the relevant PCD 150, etc. In someexemplary embodiments of the present disclosure, the requester's relatedbehavioral information may include some attributes of the relevantsurfer such as gender, age, income, etc. The requester's behavioralinformation may be retrieved from the cookie that is associated with therequest after be decompressed by CDUM 224.

The domain related behavioral information may include the number ofrequests for a certain web-page from the domain and the time of the lastrequest for this page, the number of times a certain offer (contentobject) was selected, the last time that it was selected, etc. Some ofthe exemplary counting values may be time dependent such that the valueof the counter may decrease over time. The domain related behavioralinformation may be counted and recorded in a plurality of domaincounters 222 by PRM 220. In some exemplary embodiments of the presentdisclosure, behavioral information may be updated and written by CDUM224 within a cookie that is related to PCD 150 (FIG. 1).

The counters may be incremented by PRM 220 each time a request for anevent that is related to the counter is identified by PRM 220.Incrementing the value of a counter may be time dependent such that thelast value may decrease according to the time difference between thelast event and the current event. Then the decreased value isincremented by one and the time of the current event is recorded. Fadingthe value of the counters may be done by using an exponential formulausing a half-life-time constant, for example. The half-life-timeconstant may have a configurable value or an adaptive one. Thehalf-life-time constant may be one property in a set of properties. Itshould be understood that the fading algorithm described above isoffered herein for exemplary purposes only and should not be interpretedto limit the present scope. Those skilled in the art will appreciatethat other exemplary embodiments of the present disclosure may use otherfading algorithms for adjustment of a counter.

PRM 220 may retrieve an associated cookie from the request, if oneexists. In one exemplary embodiment, in which the cookie is compressed,the cookie is transferred to CDUM 224. Those skilled in the art willappreciate that different methods may be used forcompressing/decompressing information written in a cookie. An exemplarycompression method may express the values of the counters using a subsetof ASCII based characters in lieu of decimal based characters. Otherembodiments may express the counters by using logarithmic numbers(integer and mantissa), for example. Still other embodiments may use thecombination of the two.

The decompressed cookie is parsed by CDUM 224 to identify a requester'sID wherein a requester's ID is an ID number that has been allocated tothe requester by PRM 220. The allocation of the ID can be done in thefirst request that was received from the relevant surfer in the currentvisit of the website. An exemplary ID may be defined randomly from alarge number of alternatives. It may be a 32 bit, 64 or 128 bit number,for example. If a requester's ID exists in the cookie, then the AST 215is searched for a section in AST 215 that is associated with therequester's ID. In some embodiments an ID can be allocated to a surferat the start of the initial visit to the site. This ID can be kept inthe cookie as long as the cookie exists. In such embodiment, a visit-IDcan be added as a supplement to the surfer ID. If a section exists, thena new entry is allocated in the relevant section of AST 215 forrecording the associated and behavioral information that is relevant tothe current obtained requester's request. If a section in AST 215 wasnot found, or if an ID was not found in the cookie, a new ID may beallocated for the given requester. In some exemplary embodiments of thepresent disclosure, a field in the cookie may point to a file stored atthe server that includes behavioral information associated with asurfer.

If a new requester's ID is allocated to the requester, then a newsection in AST 215 may be allocated by PRM 210 to be associated with thenew requester's ID and a new entry in the section is allocated forstorage of the information that is related to the current request for aweb-page.

Timer 226 may be used while managing behavioral information of a user.It may be used for timing indication on previous activities such asprevious visiting, previous purchasing, etc., which were recorded in thecookie. Further, the value of timer 226 may be used in the process oftime fading of the counters. These time records can be used aspredictive factors. The clock of timer 226 may range from a few secondsto a few minutes, for example. Timer 226 may be a cyclic counter havinga cycle of six months, for example.

Exemplary AST 215 may be a table, which is stored in a random accessmemory (RAM) that can be shared by the internal modules of CAS 200, forexample. AST 215 may be divided into a plurality of sections such thateach section is associated with a requester's ID and represents anactive surfer. Further, each section may include a plurality of entries.Each entry may be associated with a request of a surfer from the websitePCD 150 (FIG. 1). Each entry may have a plurality of fields for storinginformation that may be used in the prediction process.

Each entry may include fields such as, but not limited to, the receipttime of the relevant request, associated information retrieved from therelevant request, etc. Exemplary associated information may be such as,but not limited to, the type and the version of the browser applicationused for requesting the web-page or the URL used by a surfer forrequesting the web-page along with the parameters that are attached tothe URL.

In some embodiments of the present disclosure, some the URLs may includecontent grouping information such as, but not limited to, attributes ofthe content objects. Exemplary attributes may be the cost of a product,the brand of a product, vacation information, etc.

Other fields in an entry of AST 215 may store updated requester relatedbehavioral information. Still other fields may store domain relatedbehavioral information that was valid when the previous web-page wassent from the domain to the requester. Yet another group of fields maystore management information that is related to the operation of CAS 200and indicate whether the current section is associated with a randomsurfer or a personalized surfer, for example. A common surfer may be asurfer for whom the object was presented as a result of using theobject's prediction module. A personalized surfer can be referred alsoas common surfer. A random surfer may be a surfer for whom the objectwas selected randomly to be presented. A random surfer can be referredalso as a control surfer. In some embodiments a random surfer can bedefined per a slot, or one or more slots, in a certain web-page or inone or more web-pages for a current visit.

Some embodiments of AST 215 the entries may comprise also fields forstoring information related to a current visit. Such field can store thestarting time of the visit, the number of times that the URL associatedwith the entry was requested during a certain visit, etc.

In some embodiments of CAS 2000 the AST 2150 may have additional fields.These additional fields can be used for storing data that is related todifferent KPI. In such embodiments a feedback from the content server ora managing server of the domain may be received. The feedbackinformation may include information regarding a purchasing decision,which was made in association with the URL of the entry. In some casesalso the created revenue as well as an identification of the order ofthe purchase is delivered with the feedback and is stored at therelevant field of the entry, etc.

New sections and new entries in AST 215 are allocated by PRM 210.Different modules of CAS 200 may read and write information from/to theAST 215, as disclosed below relative to the description of the othermodules of CAS 200. Once every configurable period, AST 215 may bescanned by MPM 260 looking for one or more inactive sections of AST 215.Exemplary periods may be in a range from a few seconds to a few tens ofminutes. An inactive section is a section that is associated with avisit of a surfer in the web site. The decision when the visit isterminated is when the time period between the last received request(the time associated with the last entry of the section) is longer thana certain configurable value. Entries of inactive sections of AST 215may be released after the stored data in the entries of the visit(section) is copied to the appropriate PDC 240. The configurable valueof the time period can be between tens of minutes to few hours, between30 minutes to an hour for example. A common value is 30 minutes from thelast activity in the site.

After allocating an entry for the current request in the appropriatesection of AST 215, PRM 210 may write the retrieved associatedinformation in the appropriate fields of the entry. A requester'srelated behavioral information may be retrieved from the decompressedcookie. The appropriate counters of the requester's retrieved relatedbehavioral information may be updated. The received value of theappropriate counters may be manipulated to include the time affect ofthe period from the last visit (i.e., the fading affect). The timeadapted value may be incremented by one, counting the current visit. Theupdated value may be stored in the relevant field of the entry. In someembodiment, in which the new entry includes a field for storing thenumber of times that this web-page has been requested during the newvisit, the value one (reflecting the first request) is stored in thatfield.

PRM 210 may determine whether a requester that was assigned a new IDvalue can be designated as either a “control” or “common” requester. Thedecision may be based on configurable parameters, which may depend onthe mode of operation of CAS 200, such as, for example, whether CAS 200is operating in a “learning” mode or an “ongoing” mode. The decision onhow to label the requester may be written in the appropriate field inthe associated entry or section. This configurable parameter can be oneof the parameters in a set of properties.

In the case that the requester has a valid ID, the URL associated withthe request is parsed in order to determine which web-page waspreviously delivered to the requester. The determination of thepreviously delivered web-page may indicate the stimulus that promptedthe user to make the current request. Once determined, the requester'ssection in AST 215 is searched for an entry that is associated with apreviously delivered page (PDP). An entry in which the recommendedcombination includes a link to the current received request, forexample. If such an entry is found, then an indication of “success” iswritten in association with the combination of the requested object andthe link design (i.e., slot or redirection button). In other exemplaryembodiment of a CAS, information on a PDP can be received frominformation stored in the cookie. Alternatively the information can bereceived from the web-site by using an API. The success indication maybe marked in a response field that is associated with the combinationobject and slot of the entry that includes the PDP that prompted thecurrent requested object. While processing the information that isstored in the AST, object and slot combinations that do not have asuccess indication may be marked as “failures.”

After processing the request, PRM 210 may replace the cookie, or write anew cookie if the request does not already include a cookie, withupdated behavioral information. Subsequently, the request with the newcookie may be transferred to one of the content servers 152 (FIG. 1) viaHTTP proxy 210. More information on the operation of PRM 210 isdisclosed below in conjunction with FIG. 4.

An exemplary MLFH 230 may comprise a cookie update and compressionmodule (CCM) 232. MLFH 230 receives, via HTTP proxy 210, markup language(ML) files such as HTML files, for example, that represent the requestedweb-pages sent from content servers 152 toward ST 110 (FIG. 1). Eachreceived ML file is parsed and the requester's ID retrieved from thecookie. In addition, the web-page ID may be defined. Based on therequester's ID, the relevant entry in the requester section of AST 215is retrieved and parsed and the requester's assigned type (“control” or“common”) is determined.

For a “control” requester, based on the web-page ID, MLFH 230 mayrandomly configure the received ML file. The configuration may includethe set of links (i.e., slots or redirection buttons) in the web-pageand an assigned object for each one of the links in the set. Theassigned object may be selected randomly from a group of objects thatmay be presented on the requested web-page, as determined by ID of theweb-page. In the case that the requester is not a “control” requester,the location of the entry in AST 215 that is relevant to the currentpage is transferred to one of the POSM 250 associated with the page IDof the current received ML file. In response, POSM 250 may initiate aselection process for determining the predicted configuration (objectand slot) of the received ML file. As an example, the predictedconfiguration may be the configuration which maximizes the predictedexpected value obtained by presenting the particular configuration.

In some embodiments the expected value can be a KPI, which reflects theKPI of the relevant requested web-page taking into consideration thevisit of a surfer. Yet in some embodiments of CAS 2000, POSM 250 may usedifferent types of predictive models per different requested web-pages.The type of the predictive models can be based on the type of the KPIaccording to which the predictive model was built to maximize that KPI.In some embodiments of CAS 2000, in different period of time the POSM250 may automatically determine which type of predictive model to usefor the same requested web-page.

When MLFH 230 has defined a configuration (randomly selected orpredicted), it may manipulate the received ML file in order to reflectthe configuration. Per each set (object and slot), the URL of theselected object may be assigned to the relevant link (slot) in the MLfile. In addition, the entry in AST 215 may be updated to include theconfiguration of the page. Fields of the relevant entry in AST 215 thatneed to be included in an updated cookie, such as, but not limited torequester ID, updated page ID counters, and timers, are retrieved fromAST 215 by MLFH 230. Some of the cookie fields are compressed by CCM232, according to one of the compression methodologies previouslydescribed. The compressed cookie is added to the header of the modifiedML file. The modified ML file with the new cookie may then betransferred toward the requesting ST 110 (FIG. 1) via HTTP proxy 210.More information on the operation of MLFH 230 is disclosed below inconjunction with FIG. 6.

Each PDC 240 is associated with a web-page that is stored in one of thecontent servers 152 (FIG. 1) and includes one or more links (slots) aswell as a group or set of optional objects that may be assigned to thoseslots. Exemplary PDC 240 stores and manage the sampled data that isneeded for calculating predictive models for objects that are associatedwith the given web-page. An exemplary PDC 240 may comprise an eventlogger (ELOG) 242, an observation manager module (OMM) 246, and/or anobjects historical database (OHDB) 248 that may include a plurality ofdatabase couples 249 as & of to 249 ns & nf. Each couple is associatedwith an object (objects a-n that may be assigned to the page). Eachcouple of databases, one database, 249 af for example, stores records ofevents in which a link to the relevant object (object a in this example)appeared in a delivered web-page but the object was not selected. Thesecond DB 249as stores records of events in which the relevant object(object a in this example) was selected, for example.

In some embodiments of CAS 2000 the PDC 2450 may comprise two or moreKPI-objects-historical DB (KPIOHDB) 2480 a-n. Each of that two or moreKPIOHDB 2480 a-n can be used for storing data that is related to adifferent KPI. In such embodiments each KPIOHDB 2480 a-n may include aplurality of database couples 249 kas & kaf to 249kns & knf Each coupleis associated with an object (objects a-n that may be assigned to thepage) and a type of KPI. Each couple of databases, one database, 249 kaffor example, stores records of events in which a link to the relevantobject appeared in a delivered web-page but the KPI related to the DBand the object was not achieved. The second DB 249 kas stores records ofevents in which the relevant object was selected and the KPI related tothe object and the DB was achieved, for example.

In some exemplary embodiments the number of DB per object can be otherthan two DB (success; fail). For example, when a KPI has several values,zero dollars, one to ten dollars, 10 to hundreds dollars etc. In such acase three or more DBs can be used, one per each range of revenue.

From time to time, one or more entries of an inactive section may beretrieved from AST 215 by MPM 260. These entries may be sorted accordingto the web-pages that are associated with the entry and copied to ELOG242 of the relevant PDC 240. Wherein each PDC 240 is associated with oneweb-page of the one or more web-pages, each one of these entries, in AST215, may be transferred as a record into ELOG 242 and the inactiveentries released from AST 215. In some embodiments copying the inactivesession can be implemented when it is determined that the visit which isassociated with the section was terminated.

OMM 246 manages the stored records that are related to its associatedweb-page. In order to reduce the cost and the complexity of the PDC 240storage volume, the storage volume may be divided into two types, shortterm (ELOG 242) and long term (the bank of databases OHDB 248).Periodically, once in a configurable transfer period (TP), an hour forexample, the records in the ELOG 248 may be copied into appropriate ODB249 and then released. The TP can be a parameter in a set of properties,for example.

In one embodiment of PDC 240, OHDB 248 is managed in a cyclic mode. Thevolume of each ODB 249 is divided into a plurality of sub-ODB. Eachsub-ODB is associated with a transfer period. A transfer period, forexample, may be the time interval between transferring the data of ELOG242 to OHDB 248 and the number of sub-ODB may be a configurable numberthat depends on the volume of each ODB 249 and the number of records forsub-ODB that are needed. The number of records in sub-OBD may varyaccording to the mode of operation, i.e. “learning” or “ongoing” mode,for example.

It is conceivable that the number of records copied from ELOG to asub-ODB may be larger than the size of the sub-ODB. In such a scenario,dropping of records may be required. It is anticipated that in order tokeep the integrity of the sampled data when record dropping is required,the sample ratio of success-records copied to ODB 249 s and the sampleratio of the fail-records (not having a success indication) copied intoODB 249 f must be taken into consideration while preparing thepredictive model. More information on the operation of PDC 240 isdisclosed below in conjunction with FIG. 5.

Each POSM 250 is associated with a web-page that is stored in one of thecontent servers 152 (FIG. 1) and includes one or more links (slots) anda group of optional objects that may be assigned to those slots. In anexemplary embodiment of the present disclosure, POSM 250 may receive,from MLFH 230, a pointer to an entry in AST 215 that is associated witha markup language file (HTML, for example) currently processed by MLFH230. After processing the information that is retrieved from therelevant entry in AST 215, POSM 250 may deliver a predictiveconfiguration, which is a set of pairs, each pair comprised of a slotand an object, to be embedded in the markup language file that iscurrently handled. An exemplary POSM 250 may comprise a predictivefactors buffer (PFB) 253, an object selecting processor (OSP) 255 and abank of current predictive models that includes a plurality of object'spredictive model (OPM) 257 a to 257 k. Each OPM 257 is associated withan object that may be presented on the currently processed HTML file.

In some embodiments of CAS 2000 the bank of current predictive modelsincludes a plurality of object's predictive model (OPM) 257 a to 257 k .Each object current predictive OPM 257 is associated with an object fromthe ‘a’ to ‘k’ objects of the page and the KPI according which the modelwas created. This model can be replaced automatically when a model whichis based on other type of KPI is ready.

A plurality of predictive factors may be retrieved by OSP 255 from therelevant entry in AST 215 and stored in PFB 253. Different methods maybe used for determining which configuration of a delivered web-pagemaximizes the expected predicted value obtained by presenting theconfiguration. In one exemplary method for defining the predictiveconfiguration, each one of the OPM 257 a-k may be processed eithersingularly or in parallel. Per each OPM 257 a-k, one or more relevantpredictive factors are copied from PFB 253 into the relevant location inOPM 257 and the predictive value of the object, as well as thepredictive value of the configuration (set of objects and slots), iscalculated and written in a table of prediction values. Aftercalculating the predictive value of a first object and the recommendedconfiguration of the page in view of the first object, OSP 255 mayrepeat the process for the remaining one or more objects.

After calculating the predictive values and the configuration per eachobject, OSP 255 may scan the table of prediction values in order todetermine which web-page configuration (set of slots and objects)without conflicts has the highest probability of being selected by asurfer. The identified configuration may be stored in the relevant entryof AST 215 and indication that a selection was made may be sent to MLFH230.

An alternative embodiment of OSP may use another exemplary method, suchas exhausting search, for defining the predictive configuration for therequested web-page. In such an embodiment, the table of predictionvalues may include an entry per each possible configuration(permutation) of the web-page. Each entry in the table conceivable mayhave a plurality of fields wherein each field may be associated with anoptional slot. Per each cell (a junction between a row and a column inthe table), the OPM 257 of the object that is associated with the slotmay be fetched and calculated in view of the other configuration pairsof the web-page. The predictive value of the configuration of theweb-page may be calculated as the average of the predictive value ofeach cell in the row such that the configuration with the highest valuemay be selected as the predictive configuration. More information on theoperation of POSM 250 is disclosed below in conjunction with FIG. 7.

MPM 260 manages the operation of CAS 200. It may comprise a predictionmodel builder (PMB) 262, a manager module 264 and a predictive modelmonitoring module (PMM) 266. Exemplary manager 264 may communicate withthe administrator of PCD 150 (FIG. 1) in order to collect information onthe various web-pages such as, but not limited to, which optionalobjects may be associated with a web-page, which slots the optionalobjects may be assigned, the value of each object to the owner of thecontent server, the time of the day in which an object may be presented,etc.

Among other tasks, manager module 264 may define the operation mode ofCAS 200. It may determine whether to work in a learning (training) modeor in ongoing mode, for example. In some embodiments the operation modecan be defined per a certain web-page. Yet in alternate embodiments, theoperation mode can be defined per web-page and per KPI. Further, managermodule 264 may get monitoring reports from PMM 266 and generate adecision as to whether the current predictive models are valid, needtuning or need replacing. Manager module 264 may further communicatewith the administrator of PCD 150 (FIG. 1) in order to deliver reportsor gather information on new pages, expired pages, etc.

On a configurable schedule, manager module 264 may scan the AST 215 andidentify any inactive sections. An exemplary scanning period may be inthe range of few minutes to a few hours. An inactive section is asection that the time period between the last received request (the timeassociated with the last entry of the section) and the scanning time islonger than a certain configurable value. Manger module 264 may sort theentries of each inactive section according to their associated web-page.Each identified entry in the inactive section may be copied into theELOG 242 that is associated with the PDC 240 assigned to the specificweb-page. After the data of the inactive entries of AST 215 has beencopied, the inactive entries may be released. More information on theoperation of Manager module 264 is disclosed below in conjunction withFIGS. 3A & 3B.

An exemplary PMB 262 may be operable to create a plurality of predictivemodels wherein each predictive model may be associated with an objectthat may be presented in a certain web-page. In embodiments of CAS 2000each of the predictive models may be associated with an object that maybe presented in a certain web-page and a KPI that is associated to thatmodel and that object. In some embodiments of CAS 200 or 2000, when thevisit is considered while preparing a predictive model, the predictivemodel depends also on the number of times the relevant web-pages wasrequested during a certain visit.

A predictive model may include one or more predictive formulas. In orderto calculate a predictive formula, an exemplary PMB 262 may use a knownpredictive algorithm such as, but not limited to, logistic regression,linear regression, decision tree analysis, etc. Those and otherspredictive algorithm are well known to a skilled person in the art andwill not be further described. Further, some exemplary embodiments ofPMB 262 may use stepwise methods while calculating the predictiveformula. An exemplary predictive model may include one or morepredictive factors wherein each set of values of the predictive factormay be associated with a coefficient in a predictive formula. Exemplaryvalue sets may be a list of values or a range values. The coefficientmay represent the effect of a predictive factor on the prediction thatthe relevant content object will be selected by a surfer. Exemplarypredictive factors may be such as, but not limited to, the day in theweek, the hour, the browser type, the number of visits to the site, thecontent object that is presented in another selecting button in the sameweb-page, the time from the last visit, a surfer's attribute, a contentobject attribute, etc. The different predictive factors can be processedby protocols such as IP protocol, text protocols, and time protocols.Some of the predictive factors can include set of words, such as searchwords, etc.

PMB 262 may use one or more sets of properties which can be used duringthe preparation of the predictive model. Each set of properties may becomposed of a set of parameters that can be used while preparing aprediction model. An exemplary parameter may be an “aging” parameterthat assigns a weight to an entry based on its age. Other parameters maydefine the minimum number of appearances required for a predictivefactor to be considered relevant. Another parameter may define theminimum predictive score value required for a predictive model to beprocessed further, and so on.

The process of preparing an object's prediction model may includeorganizing the raw data from success and fail ODB 249 into an object'stable. Each entry from the ODBs 249Ns & 249Nf is copied into a row(record) in the object's table. The rows are sorted by time, regardlessof success indication. Each column in the object's table may beassociated with a parameter of the record, which is stored in the row.Exemplary columns may be the representative weight of the record (theweight may reflect the relative dropping value of the ODB from which therecord was copied), the result, success or failure designation, orvarious predictive factors that are stored in the relevant record.Exemplary predictive factors may include relevant URL keys that wereembedded within the associated information that was stored in the recordwherein some of the URL keys may include attributes of the requestedweb-page such as behavioral information values In some embodiments, thecookie may include one or more attributes of a surfer and thoseattributes may be associated with certain columns in the object's table.The object's table may be divided into a validation table and a trainingtable. The training table may be referred as an analyzing table.

After organizing the object's table, a bin creating process may beinitiated. A bin may be a set of values of a certain predictive factorthat differs from the other sets of values, of the predictive factor, bya certain variance in prediction efficiency. The variance can be anexplained variance and can represent a significant value. When thepredictive factor is nominally scaled, then a bin may be a list ofnames. When the predictive factor is ordinarily scaled, then the set ofvalues may be an interval of values. The explained variance thresholdand the significance desired may be parameters in the set of properties.The significance can be based on difference from an average value, forexample. The overall prediction efficiency of a bin may be calculated asthe percentage of success compare to the total number of records in thetraining table that belong to the same set (the same bin), while takinginto account the representative weight of the records when the bin istrue and when the bin is false.

An exemplary bin may be the time interval between 8:00 am to 2:00 pm,for example. A bin table is created based on the training table bydividing each column of the training table into one or more bins (eachbin represented by a column in the bin table). Each cell in the bin'stable may represent a true or false value. The value may be “true” ifthe value in the relevant cell in the training table is in the bin'sinterval and false if the value in the relevant cell in the trainingtable is not in the bin's interval.

In addition, a legend may be associated with the bin's table. The legendmay define the predictive factor from which the bin was processed, theinterval that the bin includes and the predictive efficiency of the bin.The process of creating a bin's table is repeated per each set ofproperties.

The bin's table and its legend may be loaded into a predictive modelprocessor. The predictive model processor crunches the numbers accordingto a well known predictive algorithm. Exemplary predictive algorithm canbe such as, but not limited to, logistic regression, linear regression,decision tree analysis, etc. Some exemplary embodiments of the presentdisclosure can use linear or logistic regression, with or withoutstepwise methods, while calculating the predictive formula. Thecalculated predictive model that was created based on the first set ofproperties is stored. PMB 262 may repeat the actions of creating asecond bin's table and a second predictive model based on the second setof properties. The second predictive model may be stored as well and PMB262 may continue the process for the remaining property sets. Anexemplary PMB 262 that is installed in an exemplary CAS 2000 may repeatthe actions of creating a second bin's table and a second predictivemodel based on the second KPI. The second predictive model may be storedas well and PMB 262 may continue the process for the remaining KPIs.

When a set of predictive models is calculated, one per each set ofproperties, each one of the predictive models is applied on the recordsof the validation table. The results of the predictions are comparedwith the actual recorded responses of surfers and a prediction score isgranted to the predictive model. An exemplary prediction score may bethe percentage of the successful predictions, when compared to theactual recorded responses, out of the total records in the validationtable. The group of calculated predictive models, one per each set ofproperties, and or KPIs may be sorted according to the respectiveprediction scores. The models having a score below a predeterminedminimum (one of the parameters in a set of properties) may be ignored.Then an object prediction model is calculated as a weighted average ofthe predictive models having prediction scores above the predeterminedminimum.

In order to reduce the number of ignored prediction models, anotherexemplary PMB 262 may further process the comparison between theprediction results of the different models in order to adjust theassociated property sets. For example, if one or more predictive modelshave a prediction score below the limit and use property sets in whichthe half-life time constant has a large value, the value may be adjustedto a smaller one, for example.

PMB 262 may repeat the above described process for each one of thepossible objects in a certain web-page and then it may repeat thisprocess for each web-page. In an alternate embodiment of the presentdisclosure, MPM 260 may comprise a plurality of PMB 262. Each PMB 262may be associated with a web-page, for example. Embodiments of PMB 262that are embedded within exemplary CAS 2000 can repeat those actions forcreating predictive models per each KPI. More information on theoperation of PMB 262 is disclosed below in conjunction with FIGS. 9A &9B.

An exemplary PMM 266 monitors the plurality of predictive models whereineach predictive model may be associated with an object that can bepresented in a certain web-page. PMM 266 may be adapted to monitor theperformance of each one of the object's prediction models that arecurrently used by CAS 200. Per each web-page, an exemplary PMM 266 mayperiodically monitor the ELOG 242 of the PDC 240 that is assigned to thecurrent monitored web-page. The period between consecutive monitoringmay be a configurable period that depends on the mode of operation ofCAS 200. In “learning” mode (training mode), the configurable timeperiod, Tmt, may range from a few minutes to tens of minutes. During“ongoing” mode, the configurable period, Tmo, may be tens of minutes toa few hours, for example.

An exemplary PMM 266 that is installed in an exemplary CAS 2000 maymonitors the plurality of predictive models wherein each predictivemodel may be associated with an object that can be presented in acertain web-page and one type of KPIs.

Per each object that may be associated with the web-page, PMM 266 mayscan the records in ELOG 242 looking for records in which a deliveredweb-page presents the relevant object to be selected by a surfer. Eachfound record may be subsequently parsed in order to determine whetherthe record was executed by a “common” surfer (a surfer for whom theobject was presented as a result of using the object's predictionmodule) or a “control” surfer (a surfer for whom the object was selectedrandomly to be presented). The record may be further parsed in order todetermine the response of a surfer, whether the object was selected by asurfer (success) or not (failure). At the end of the process, PMM 266may calculate two probability values per each object's prediction model,i.e. the probability of success of a “common” surfer and/or theprobability of success of a “control” surfer. The two probability valuesper each object's prediction model in the web-page may be written in aweb-page comparison table. The web-page comparison table may betransferred to the manager module 264 and/or PMB 262. After processingthe records stored in ELOG 242, an indication may be sent to OMM 246 ofthe same PDC 240 informing the OMM 246 that it may initiate the processof transferring the information from ELOG 242 to OHDB 248.

An exemplary PMM 266 that is installed in an exemplary CAS 2000 mayrepeat the actions of monitoring a predictive model per each KPI. Insuch embodiment the web-page comparison table may be associated with aweb page and one of the KPIs.

At this point, PMM 266 may repeat the process for the next web-page andcontinue in a loop. In an alternate embodiment of the presentdisclosure, MPM 260 may comprise a plurality of PMM 266 wherein each PMM266 may be assigned to a web-page. More information on the operation ofPMM 266 is disclosed below in conjunction with FIG. 8.

In addition to the above mentioned elements, exemplary embodiments ofCAS 200 or CAS 2000 may comprise a non-transitory storage mediumreadable by a processor and storing instructions for execution by theprocessor for performing the capabilities of the exemplary CAS 200 orCAS 2000. The storage medium can be included as a part of a computersystem of CAS 200 or CAS 2000 or sold separately.

The non-transitory storage medium can be an electronic, magnetic,optical, electromagnetic, infrared, or semiconductor system (orapparatus or device) or a propagation medium. Examples of a computerreadable medium include a semiconductor or solid state memory, magnetictape, a removable computer diskette, a random access memory (RAM), aread-only memory (ROM), a rigid magnetic disk and an optical disk.Examples of optical disks include compact disk-read only memory(CD-ROM), compact disk-read/write (CD-R/W) and DVD, etc.

FIGS. 3A & 3B depict a flowchart illustrating relevant processes of anexemplary method 300 used by some embodiments of the present disclosurefor managing the operation of CAS 200 (FIG. 2 a) or CAS 2000 (FIG. 2 b).Method 300 may be implemented within the manager module 264 (FIG. 2),for example. In order to eliminate redundancy of information, in thefollowing paragraphs actions of method 300 that implemented in CAS 200or CAS 2000, which are similar, are disclosed once, in relation with CAS200. Base on reading the description of method 300 for CAS 200, anordinary skilled person in the art is expected to understand how thosesimilar actions are operating in relation with CAS 2000. In some actionsof CAS 2000 that may operate in a different way or may need additionalinformation, this information is disclosed in addition to theinformation related to CAS 200.

Method 300 may be initialized 302 upon “power on” status or afterresetting the CAS 200 and may run in a loop as long as the CAS 200 isactive. During the initialization process 302 & 304, the manager 264 maybe loaded with information such as information on relevant resourcesthat stand for the internal modules of CAS 200. Accordingly, the manager264 may allocate resources to the other modules such as PRM 220, PDC240, POSM 250, etc. The AST 215 (FIG. 2) may be created and introducedto the appropriate modules. The clock of CAS 200 may be adjusted.Further, the clock may use a proprietary format. For example, the clockmay count minutes in continuous mode from 00:00 on the first day ofJanuary until 23:59 of Dec. 31. In alternate embodiments, the clock maybe reset every six months, etc.

When an embodiment of method 300 is implemented in CAS 2000, the loadedinformation may comprise information regarding two or more KPI. Thisinformation can include one or more set of parameters per each KPI.Parameters such as value of each KPI for each object, “aging” for eachKPI, per each KPI the minimum number of appearances required for apredictive factor to be considered relevant. Another parameter maydefine per each KPI the minimum predictive score value required for apredictive model to be processed further, information when to switchfrom a predictive model based on one type of KPI to a predictive modelbased on another KPI which is more important to the website owner, etc.Accordingly, the manager 264 may allocate resources to the other modulessuch as PRM 220, PDC 2400, POSM 250, etc. The AST 2150 (FIG. 2 b) may becreated and introduced to the appropriate modules.

After setting the internal models, the manager 264 may get 304 updatedinformation from the content servers 152 and/or the administrator of PCD150 (FIG. 1). Information such as, but not limited to, informationrelated to a web-page, optional objects that may be presented, optionalslots (redirection buttons or links), priorities and the value (KPI) ofeach object, etc. When method 300 is implemented by CAS 2000, theinformation may include the value of each type of KPI of a certainobject.

At the end of the initiation process, MLFH 230 (FIG. 2) may beinstructed 306 to treat all responses (ML files) as if each wasassociated with a “control” surfer. In response, MLFH 230 may randomlyselect objects to be placed and presented in a requested web-page. Othermodules such as PRM 220, OMM 246 and PMM 266 (FIG. 2) may be instructedto work in a “learning” or “training” mode. During the learning mode,PRM 220 may mark all surfers as “control” surfers. Further, OMM 246 andPMM 266 may operate at higher rates than when the embodiment is in“ongoing” mode. Stage Timer (ST) is reset and used to define the timelimit of the current mode of operation.

At such point, method 300 may wait 310 until the value of ST reaches aconfigurable value predefined to correspond to the “Learning Period”.The “Learning Period” may be in the range of few tens of minutes to afew days, depending on the volume of requests sent toward PCD 150 (FIG.1), for example. If 310 the ST reached the value of the “LearningPeriod,” then PMB 262 (FIG. 2) may be checked 312 in order to determineif 320 it was successful in preparing a set of predictive models forobjects. If the set is not ready, method 300 may inform 322 anadministrator of PCD 150 (FIG. 1) about the number of cycles of the“Learning Period” that were completed without success. In such a case,ST may be reset and method 300 looped to action 310.

In some embodiments of CAS 2000, in which method 300 is implemented fortwo or more KPIs, a sub-process that includes actions 306 (FIGS. 3 a) to364 (FIG. 3 b) can be executed in parallel in several instances, eachsuch a sub-process can be associated with a type of KPI. Eachsub-process may have a separate ST and a separate time limiter per eachKPI and each operating mode (learn period, monitor period and ongoingperiod.

If 320 a set of object's predictive models is ready, then the set isloaded 324 into POSM 250. Other internal modules such as PRM 220, MLFH230, OMM 246, PMB 262, OSP 255 and PMM 266 (FIG. 2) may be instructed tochange mode into a “monitoring” mode (period). The monitoring mode isused for tracking the individual predictive models. During themonitoring mode, PRM 220 designates a certain percentage (a configurablenumber in the range of 5% to 50%, for example) of surfers as “random”surfers. PMM 266 may compare the probability of success of randomsurfers to the probability of success of the personalized surfers andcreate a set of comparison tables, one per each object's predictivemodel. The comparison tables may be used by manager 264 (FIG. 2) inorder to determine the performance of each set of object's predictivemodels.

In some embodiments of CAS 2000, in which method 300 is implemented fortwo or more KPIs, each sub-process can be associated with a type of KPI.Each sub-process, which includes actions 306 (FIGS. 3 a) to 364 (FIG. 3b), may have a separate comparison tables one per each KPI.

In an alternate embodiment of the present disclosure, the monitoringmode may be implemented on a page level and not on an object level. Insuch an embodiment, the two counters, ST and a model's lifetime counter,are reset. The model's lifetime counter may be used for defining theoverall time limit or maximum age of the predictive models. A typical,and exemplary, maximum age may be 24 hours, for example.

Then method 300 waits 330 until the value of ST reaches a configurablevalue predetermined to define the “Monitor Period.” The “Monitor Period”may be in the range of few tens of minutes to a few hours, depending onthe volume of requests which were sent toward PCD 150 (FIG. 1), forexample. If 330 the ST reached the value of the “Monitor Period,” thenPMM 266 (FIG. 2) may be requested to deliver a set of web-pagecomparison tables, one per each web-page 332. A web-page comparisontable may comprise the two probability values per each object'sprediction model that may be assigned to a given web-page. The twoprobability values may represent the probability of success of apersonalized surfer and the probability of success of a random surfer.

In some embodiments of the present disclosure, in which the monitoringmode is implemented at a page level, the probability values may becalculated at such level. In such an embodiment, a success of a page maybe defined as responding to one of the presented objects. In some cases,each observation may be weighed by the percentage of the utility valueof the selected object from the utility value of the page itself. Insome embodiments of CAS 2000, in which method 300 is implemented for twoor more KPIs, per each sub-process the selected utility value canreflects the relevant KPI associated with that sub-process.

The manager module 264 (FIG. 2) may process 332 the results from thetable and compare them to a predefined set of values that are stipulatedby the administrator of PCD 150 (FIG. 1). If 334 the results of thepredictive models are better than the required values, then method 300proceeds to action 340 in FIG. 3B. If 334, in the alternative, theresults are below the stipulated performance threshold, then PMB 262(FIG. 2) may be adjusted 336 accordingly. The set of properties whichwere used for calculating the predictive model may be adjusted, forexample. In addition, an indication may be sent to the administrator andmethod 300 may return to action 306.

For CAS 2000, which handles two or more KPIs, action 332 and 334 can beexecuted per each KPI. Thus, when a predictive model is ready for one ofthe KPI, an exemplary CAS 2000 can start the ongoing period using theready predictive model based on that KPI, while continuing the learningmode for the other KPIs. For example, due to the difference in theappearance of “clicking” versus purchasing, a predictive model based ona “click” KPI can be ready before a predictive model based on “revenue”KPI. In such a case CAS 2000 may start using the predictive model basedon “click” KPI and later, when a predictive model based on “revenue” KPIis ready, CAS 2000 can switch and use the “purchasing” KPI model.

FIG. 3B depicts the ongoing operation mode of method 300. At action 340,PRM 220, MLFH 230, OMM 246, PMB 262, OSP 255 and PMM 266 (FIG. 2) may beinstructed to change mode into an ongoing mode while the performance ofthe loaded sets of object's predictive models, one set per eachweb-page, is monitored and ST is reset. During the ongoing mode, PRM 220designates a small percentage of surfers as random surfers. PMM 266 maycompare the probability of success of random surfers to the probabilityof success of personalized surfers and create a set of comparisontables, one per each object's predictive model. The comparison tablesmay be used by manager 264 (FIG. 2) for determining the performance ofthe sets of the object's predictive models. In some embodiments of CAS2000, in which method 300 is implemented for two or more KPIs, per eachsub-process the compression tables can reflect the relevant KPIassociated with that sub-process.

Method 300 may wait 342 until the value of ST reaches a configurablevalue predefined to correlate with the “Ongoing Period.” The “OngoingPeriod” may be in the range of a few tens of minutes to a few days,depending on the volume of requests which were sent toward PCD 150 (FIG.1), for example. In a similar way the Ongoing Period may varied from oneKPI to the other. An ongoing period of a KPI that reflects the clickingrate can be shorter than the time period of the ongoing period of a KPIthat reflects purchasing, because clicking is more frequent thanpurchasing. Consequently a longer period is needed to collect therequired number of events.

When 342 the ST reaches the value of the “Ongoing Period,” then PMM 266(FIG. 2) may be requested to deliver a set of web-page comparisontables, one per each web-page; and for CAS 2000 also a set of web-pagecomparison tables per each KPI. The manager module 264 (FIG. 2) maysubsequently process the results in the table and compare them to apredefined set of values that are stipulated by the administrator of PCD150 (FIG. 1) 344. If 350 the results of the predictive models exceed therequired values, then the predictive models may be considered as validand method 300 proceeds to action 354. If 350 the results are below therequired performance, then the predictive models may be considered asnot valid and method 300 may return 362 to action 306 (FIG. 3 a) forcalculating new sets of object's predictive models.

At action 354, the value of the model's lifetime counter is checked anda determination is made 360 as to whether the value is below aconfigurable value Tmax. Exemplary Tmax may be a few hours to a fewdays, for example. If 360 the value is below Tmax, which would indicatethat the sets of object's predictive models are still valid, then the STcounter may be reset 364 and method 300 returned to action 342. If thevalue of the lifetime counter is above Tmax, then method 300 may proceedto action 362 and returned to action 306 (FIG. 3 a) to initiatecalculation of a new set of predictive models.

FIG. 4 illustrates a flowchart with relevant processes of an exemplarymethod 400 that may be used for handling surfer's requests that are senttoward content servers 152 (FIG. 1). Method 400 may be implementedwithin the PRM 220 (FIG. 2 a or 2 b), for example. Method 400 may beinitialized 402 by the manager module 264 during the “power on” processand each time the manager determines to change the mode of operation ofCAS 200 (learning, monitoring and ongoing). After initiation, method 400may run in a loop as long as the mode is not changed. In order toeliminate redundancy of information in the following paragraphs, actionsof method 400 that implemented in CAS 200 or CAS 2000, which aresimilar, are marked with similar numeral and are disclosed once, inrelation with CAS 200. Base on reading the description of method 400 forCAS 200, an ordinary skilled person in the art is expected to understandhow those similar actions are operating in relation with CAS 2000. Insome actions of CAS 2000 that may operate in a different way or may needadditional information, this information will be added.

During the initialization process 402, PRM 220 may be loaded withinformation that is relevant for communicating with other internalmodules of CAS 200, modules such as the manager 264 and AST 215 (FIG.2). In addition, AST 215 may be initialized.

After the initiation stage, method 400 may start processing surfer'srequests that are transferred via HTTP proxy 210 (FIG. 2) to a queue ofPRM 220. The next request in the queue is fetched 404 and parsed inorder to identify any attached cookie. If 410 the request does notinclude a cookie (a new surfer), method 400 proceeds to action 412 forhanding the request of the new surfer. If 410 a cookie is found,exemplary embodiments of the present disclosure may decompress 430 andparse the cookie (assuming the cookie is compressed). In other exemplaryembodiments, in which compression is not used, any identified cookie isjust parsed without prior decompression. Once parsed, a cookie issearched for a requester ID and, according to the ID, the AST 215 (FIG.2) is searched 432 for a section that is associated with the ID. If 432a section in the active session table AST 215 (FIG. 2) was not found,then method 400 proceeds to action 412. If 432 a session in the activesession table AST 215 (FIG. 2) was found, indicating that the request isassociated with a current active surfer having at least one recentrequest served by CAS 200, then method 400 proceeds to action 440 andcontinues serving the active surfer.

At action 412 the first request of a new active surfer is handled. An IDis allocated to the new surfer. The ID can be allocated randomly from alarge number of alternatives. The ID may be a 32 bit, 64 or 128 bitnumber, for example. In case that the current mode is a learning mode,then the surfer is defined as a random surfer. In case that the currentmode of operation is monitoring mode or ongoing mode. The type of thenew surfer as a personalized surfer or as a random surfer can be definedbased on the allocated ID and a percentage of surfers per each type. Inthe monitoring period, the percentage of the random surfers is largerthan the percentage of the random surfer that is associated with theongoing mode. Thus, the portion of the random surfers in a monitoringperiod is larger than the control portion during the ongoing mode. Thevalue of percentages is a configurable value depending on the trafficvia CAS 200.

A new section in AST 215 (FIG. 2) is allocated to the new active surfer.Subsequently, the new section in AST 215 is associated with theallocated requester's ID. The allocated ID and the type of the surfer(random or personalized) can be associated to that section. In the newsection, an entry is allocated for the current request. At this point,method 400 starts writing the information associated with the request tothe appropriate fields of the allocated entry. The associatedinformation may include, for example, the receipt time according to theclock of CAS 200, the URL which is associated with the request, thesource IP and the destination IP of the request, etc. In someembodiments the ID may comprise a surfer ID and a supplement ID. Thesurfer ID remains in the cookie as long as the cookie exist Thesupplement ID reflects the current visit and it can exist as long as thesurfer is active and has an entry in the AST 215.

In some embodiments that calculate predictive model per visit, thereceiving time of the first request can indicate the beginning of thevisit. In some embodiments this time can be stored in a dedicated filedof the new section that is associated with the new requester's ID. Insome exemplary embodiments of the present disclosure, the URL mayinclude one or more fields that reflect attributes of the requestedweb-page. Exemplary attributes may be the topic of the page (vacation,news, sports, etc.), classification of the advertised goods, (expensive,low cost, etc.).

The decompressed cookie, if it exists, is updated 412 or a new cookiemay be written. The cookie may include behavioral information associatedwith the surfer such as the date and time of the surfer's last visit tothe web-page, the frequency of visits by the surfer to the web-page,etc.

Other behavioral information may include one or more counters whereineach counter may count the number of events of a certain type. Exemplarycounters may count the number of the active surfer's visits to therelevant website, the number of requests for a certain web-page from thewebsite, the number of times a certain offer (content object) wasselected, etc. Updating the counters may be time dependent Such that thevalue of the counter may decrease over time, as previously described. Insome embodiments, the cookie may include labels for a surfer'sattributes. Exemplary attributes may be a surfer's age, gender, hobbies,etc. The updated information of the cookie is stored in the appropriatefields of the entry in AST 215.

In exemplary embodiments that create predictive model based on theentire visit of surfer an additional counter can be added for countingthe number of requesting the relevant webpage in the relevant visit.This counter can be reset each time a new visit is initiated by therelevant surfer to that website.

After setting the fields in the section of AST 215, the method 400proceeds to action 456 in which the requester ID is written in thecookie of the request and the request is transferred toward itsdestination (one of the servers 152 in FIG. 1) via the http proxy 210(FIG. 2). Method 400 returns to action 404 for processing the nextrequest.

In an alternate exemplary embodiment of the present disclosure, defininga surfer as a random surfer may be executed after receiving a surfer'sresponse. In such an embodiment, a surfer that responded positively maybe defined as a random surfer. One of the reasons for using this sortingmethod is to increase the size of the positive samples, since thepositive samples are less populated than non-responding surfers.

In embodiments of CAS 2000 (FIG. 2) in which two or more predictivemodels, per object, are handled simultaneously, each module isassociated with a type of KPI, process 400 can comprise two or moresub-processes, each sub-process can be associated with a type of KPI.Each sub-process can have different percentages of random surfers orpersonalized surfers for adapting sampling rate to the activity rate ofeach KPI. A sub-process that is associated with a purchasing rate, whichis less frequent than any other KPI, defining a surfer as a randomsurfer may be executed after receiving a surfer's response.

Returning now to action 432, if a section that is associated with therequester's ID exists, then a surfer's section in AST 215 (FIG. 2) isretrieved 440 and parsed. While processing the information that isstored in the AST, object and slot combinations that do not have asuccess indication may be referred to as failures. Next, a new entry inthe section is allocated for storing information that is relevant to thecurrent received request. The information associated with the request iswritten into the appropriate fields of the new entry. The decompressedcookie is updated written to the appropriate fields of the new entry, asdisclosed above in conjunction with action 412. Notably, in someembodiments the cookie may include an indication of a surfer'sattribute, while the URL may include an indication on attributes of therequested web-page.

After writing the information in the new entry, the URL that isassociated with the request is parsed 442 and the requester's section inAST 215 is searched 442 for an entry that is associated with apreviously delivered page (PDP) that points to the current requestedobject in order to determine whether the current request originated froma web page that was recently sent via CAS 200. Presumably, such aweb-page included an object and slot combination which reflects the URLthat is associated with the current received request.

If 450 such an entry is found, then a success indication is written 452in the result field that is associated with the object and slotcombination of the URL associated with the current request. At suchpoint, method 400 proceeds to action 456. If 450 a PDP is not found,method 400 proceeds to action 456.

There are some exemplary embodiments in which two or more KPI are usedor in which the entire visit is taking into consideration. Suchexemplary embodiment may record the entire visit of a user, based on theuser ID and supplement ID that reflects the visit. The record caninclude information on the surfer's requests during the visit, theresponses sent per each requests, the result of the visit (one or morepurchases done by the surfer during the visit, the revenue per eachpurchases and the total revenue, for example). At the end of the visitthe recorded information is parsed and added to the relevant entries andKPIs in the surfer's section in the AST 215.

FIG. 5 illustrates a flowchart with relevant processes of an exemplarymethod 500 that may be used for managing a page data collector module(PDC) 240 (FIG. 2 a) or 2400 (FIG. 2 b). Method 500 may be implementedwithin the OMM 246 (FIG. 2 a&b), for example. Method 500 may beinitialized 502 by the manager module 264 (FIG. 2 a&b) during the “poweron” process. After initialization, method 500 may run in a loop as longas CAS 200 is active. Method 500 may be used for transferring the storeddata from ELOG 242 (FIG. 2 a&b) to OHDBs 248 (FIG. 2 a) or KPIOHDB 2480s (FIG. 2 b) while keeping the integrity of the sampled data as arepresentative sample of a surfers' behavior. In order to eliminateredundancy of information in the following paragraphs, actions of method500 that implemented in CAS 200 or CAS 2000, which are similar, aredisclosed once, in relation with CAS 200. Base on reading thedescription of method 500 for CAS 200, an ordinary skilled person in theart is expected to understand how those similar actions are operating inrelation with CAS 2000. Some actions of method 500 which in CAS 2000 mayoperate in a different way or may need additional information compare toa similar action implemented by CAS 200, this information will bedescribed separately.

During the initialization process 502 & 504, OMM 246 may be loaded withinformation that is relevant for communicating with other internalmodules of CAS 200 such as the manager 264, PMM 266, ELOG 242, (FIG. 2)etc. In addition, information is retrieved from manger module 264 (FIG.2). The retrieved information may be relevant to the web-page, which isassociated with the PDC 240 that includes the relevant OMM 246. Theinformation may include the objects that may be associated with theweb-page and the slots by which the objects may be presented. Timers,such as the OMM timer (OT), may be reset.

After the initiation process, a determination is made 506 as to whetherthe current operating mode is a learning mode (training mode). If not alearning mode, method 500 may wait 507 for an indication from PMM 266,in which PMM 266 may inform OMM 246 that the data, which is stored inthe current ELOG 242, was inspected and that OMM 246 may starttransferring the data from the ELOG 242 toward OHDB 248 (FIG. 2). When507 an indication is received, method 500 may proceed to action 509. If506 the operating mode is a learning mode, then method 500 may waituntil OT is greater than Telog 508. Telog may be a configurableparameter in the range of a few tens of seconds to a few tens ofminutes, for example. When OT is greater than Telog, method 500 proceedsto action 509.

At action 509 a new ELOG 242 is allocated 509 for storing the futureevents that are related to the relevant web-page and method 500 beginsprocessing and transferring the stored data from the old ELOG 242 intoOHDB 248. A loop from action 510 to action 520 may be initiated and eachcycle in the loop may be associated with an optional object that may bepresented over the relevant web-page.

At action 512, the oldest sub-ODB in each one of the ODB couplesassociated with the current object, object “a” for example whichcomprises the success ODB 249 as and the failure ODB 249 af, arereleased and a new sub-ODB is allocated for storing data that istransferred from the old ELOG 242 that is related to the current object.The old ELOG 242 (FIG. 2) is searched for entries of random surfers thatinclude the current object. The result field in each entry is parsed andthe entries are divided into two groups, a success group (having asuccess indication) and a failure group (do not have a successindication). The entries in each group may be sorted by time such thatthe newest entry appears at the top of the group, for example, and thetotal number of each group calculated. In an exemplary embodiment inwhich the entire visit is used for preparing the predictive model, theinformation that is copied into the success or fail ODB 249 as&af, forexample, reflects the number of times the webpage was requested, duringthe relevant visit, until the success was reached.

The portion of the entries from each group that may be stored in theappropriate new sub-ODB is calculated 514 by dividing the number ofentries in each one of the appropriate new sub-ODB (success or failure)by the total number of entries in each group (success or failure,respectively). If at least one of the groups has a portion value smallerthan one, meaning that some of the entries of the group will be droppedin lieu of being stored in the new sub-ODB, then the smallest portionvalue may be selected for determining the number of entries from eachgroup (success or failure) that will be stored in the appropriatesub-ODB (success or failure, respectively).

The total number of entries in each group is multiplied by the smallestportion value for determining the numbers of entries (NE) from eachgroup that will be stored in the appropriate new sub-ODB. NE entriesfrom the top (the newest entries) of each group are copied 516 to theappropriate sub-ODB. If for both groups the portion values are greaterthan “one,” then all the entries of each group (success or failure) maybe copied to the appropriate new sub-ODB (success or failure,respectively). The portion value of the deletion per each group (successand failure) may be recorded in association with the success ODB 249as-ns and the failure ODB249 af-nf (respectively). In some exemplaryembodiments of the present disclosure, old entries in the success ODB249 as-ns and the failure ODB249 af-nf may be released only when thereis no free space available for storage of new observations.

After copying 516 the entries to the new sub-ODB, a determination ismade 520 as to whether additional objects may be presented in therelevant web-page. If yes, method 500 may start a new cycle in the loopfor the next object and return to action 510. If 520 there are no moreobjects, then the old ELOG 242 is released 522, timer OT is reset, andmethod 500 returns to action 506.

In some embodiments of CAS 2000, in which method 500 is implemented fortwo or more KPIs, each sub-process can be associated with a type of KPI.Each sub-process can include actions 506 to 522. Such an embodiment ofCAS 2000 may have a separate ELOG 242 (FIG. 2 b) per each KPI. Yet otherembodiments may use a single ELOG 242 (FIG. 2 b), in such embodiment,each sub-process can includes actions 506 to 520 and action 522, inwhich the old ELOG 242 is released, can be implemented once after copingthe information of the events that are associated with the last KPI, forexample. In some exemplary embodiments of the present disclosure, oldentries in the success ODB 249 kas-kns and the failure ODB249 kaf-knfmay be released only when there is no free space available for storageof new observations related to a certain key.

FIG. 6 illustrates a flowchart with relevant processes of an exemplarymethod 600 that may be used for handling web-pages, in the form ofmarkup language files (an HTML, for example), which is sent from one ofthe content servers 152 (FIG. 1) in response to a surfer's requests.Method 600 may be implemented within the MLFH 230 (FIG. 2), for example.Method 600 may be initialized 602 by the manager module 264 (FIG. 2)during the “power on” process. After initialization, method 600 may runin a loop as long as CAS 200 (FIG. 2) is active. During theinitialization process 602, MLFH 230 may be loaded with information thatis relevant for communicating with other internal modules of CAS 200,modules such as the manager 264, the plurality of POSM 250 and AST 215(FIG. 2), etc.

In order to eliminate redundancy of information in the followingparagraphs, actions of method 600 that implemented in CAS 200 or CAS2000, which are similar, are disclosed once, in relation with CAS 200.Base on reading the description of method 600 for CAS 200, an ordinaryskilled person in the art is expected to understand how those similaractions are operating in relation with CAS 2000. In some actions method600 in which CAS 2000 may operate in a different way or may needadditional information, this information will be added.

After the initiation stage, method 600 may start processing receivedweb-pages from the content servers 152 (FIG. 1) and are transferred viaHTTP proxy 210 (FIG. 2) to a queue of MLFH 230. The next packet in thequeue is fetched 604 and parsed in order to determine whether a cookieis attached. If a cookie is found, the cookie is furthered processed andthe requester ID, which is written in the cookie, is retrieved. AST 215is searched for a section that is associated with the requester ID.Starting from the last entry in the requester's section, the entry isretrieved and parsed to identify the fields that define the web-page IDof that entry. If the web-page ID dose not match the web-page ID writtenin the header of the received web-page, the earlier entry is fetched andbe handled in a similar way. If the web-page ID of that entry matchesthe web-page ID written in the header, then a surfer's type, i.e.personalized, or random is retrieved.

If 610 a surfer is designated as a random surfer, then at action 612,based on the information written in the entry in the AST associated withthe web-page ID, a list of optional objects and a list of slots in theweb-page are fetched. Per each slot, an optional object is selectedrandomly and method 600 proceeds to action 622. In an alternateembodiment, in which a surfer type (random surfer) is defined per slot,then the random selection is done only for that slot.

If 610 the requester of the web-page is not designated as a randomsurfer, then a POSM 257 is selected 614 based on the page ID. The entrynumber in AST 215 that is associated with the request for the givenweb-page is transferred to a queue of the selected POSM 250. Method 600may wait 620 until a decision from the relevant POSM 250 is receivedthat identifies the web-page configuration that has the highestprobability of prompting an active surfer to respond through theselection of one of the optional objects. The identified configurationmay include a list of slot and object combinations that have the highestassociated probabilities to highest utility to the web-site owner. Thenmethod 600 proceeds to action 622.

At action 622, either the identified configuration of the web-page orthe randomly selected configuration of the web-page, having a set ofobject and slot combinations, is written 622 to the appropriate fieldsin the relevant entry of AST 215. The HTML file is modified to includethe selected one or more objects, each object being associated with anappropriate slot. The updated cookie, which was prepared by PRM 220,manipulated by the relevant content server 152 (FIG. 1) and stored inthe entry, is compressed and added as a cookie to the modified ML file.The requester's ID is also added and associated with the cookie. Themodified ML file with the cookie is transferred to HTTP proxy 210 andsent toward the destination of the ML file of the requester surfer.Method 600 returns to action 604 and starts processing a new received MLfile.

Yet in an alternate embodiment of CAS 200 or 2000, the selectedconfiguration is sent by the API to the content server 152 (FIG. 1). Thecontent surfer modifies the requested ML file according to the selectedconfiguration and sends the modified ML file to the requesting surer.

In an alternate embodiment, the set of pairs in the configuration mayinfluence the design of a web-page. In such an embodiment, a pair mayinclude a design feature in lieu of a slot and the value of the designfeature in lieu of the object. Exemplary design features may bebackground color, font size, font type, etc. Exemplary correspondingvalues may be red, 12 points, Times New Roman, etc.

FIG. 7 illustrates a flowchart with relevant processes of an exemplarymethod 700 that may be used for calculating the prediction value foreach optional configuration (set of optional-object and slotcombinations) from a plurality of optional configurations of a deliveredweb-page. The prediction value may represent the predicted value of therelevant KPI that the requester of the delivered web-page will selectone of the presented objects while observing the delivered web-page. Theconfiguration of the web-page that reflects the highest possible utilitycan be defined as the predictive configuration. Method 700 may beimplemented within the OSP 255 (FIG. 2), for example. Method 700, at aselected POSM 250 that is associated with a delivered web-page, may beinitialized 702 upon receiving from MLFH 230 (FIGS. 2 a&b) an entrynumber in AST 215 (FIG. 2). The entry is associated with the deliveredweb-page that is currently processed (action 614, FIG. 6) by MLFH 230(FIG. 2).

In order to eliminate redundancy of information in the followingparagraphs, actions of method 700 that implemented in CAS 200 or CAS2000, which are similar, are disclosed once, in relation with CAS 200.Base on reading the description of method 700 for CAS 200, an ordinaryskilled person in the art is expected to understand how those similaractions are operating in relation with CAS 2000. In some actions method700 in which CAS 2000 may operate in a different way or may needadditional information, this information will be added.

After initiation, method 700 may fetch 704 the entry from AST 215 thatis associated with the request for this delivered web-page. The entrymay be parsed and a plurality of predictive factors can be retrievedfrom the associated information, behavioral information, and groupinginformation (if such exists). Exemplary predictive factors that may beretrieved from the associated information stored at the entry mayinclude the receipt time of the request, the URL keys associated withthe request, etc. Exemplary predictive factors that may be retrievedfrom the behavioral information may include the elapsed time from thelast visit of the requester in PCD 150 (FIG. 1), the number of positiveresponses from the requester, etc. Exemplary grouping information mayinclude indications on a surfer's attribute such as, but not limited to,gender, age, purchasing habits, etc. Further, the indication may becoded in a code which is unknown to CAS 200 (FIG. 2). The retrievedpredictive factors may be stored in PFB 253 (FIG. 2) of the POSM 250that is associated with the delivered web-page.

The optional configurations of the requested web-page are calculated704. A configuration may define a set of the slots, in which an optionalobject may be presented, and a selected optional object that ispresented in each slot (Slot/Optional-object). Usually, an exemplaryweb-page has a certain amount of slots and a certain amount ofoptional-objects such that the number of possible configurations may bedefined. The optional configurations may include all possiblecombinations of slots and objects, for example. A table of predictionvalues may be allocated 704 for storing the calculated prediction valuefor each optional configuration.

An exemplary table of prediction values may include a plurality ofentries. Each entry may be associated with an optional configuration ofthe delivered web-page. Further, each entry may include a plurality offields and each field may be associated with a specific slot number withan additional field defining the predicted value of each configuration.Each cell (a junction of a configuration and a slot number) in the tablemay include the calculated prediction value of the object that isassigned to the slot in that configuration, wherein the calculation ofthe predictive value is done in view of the rest of the slots andoptional-objects in the configuration. The prediction value of theconfiguration may be the sum of the predictive values of each one of theslot fields in this entry.

In some exemplary embodiments of the present disclosure, the number ofoptional configurations may be calculated once, during theinitialization of POSM 250 or upon receiving information on changes inthe associated web-page. In such case, an exemplary table of predictionvalues may be defined once and allocated again and again each time therelevant web-page is delivered.

After allocating the table of prediction values, a loop between actions710 to 740 may be initiated to process all the possible configurationsof the web-page written in the allocated table. For each configuration,an internal loop is initiated between actions 720 to 730. The internalloop may be executed per each slot of the configuration. An OPM 257 a-n(FIG. 2) that is associated with an object that was assigned to a firstslot of a first configuration in the table is fetched 722. Relevantpredictive factors included in the model are retrieved 722 from PFB 253and placed in the appropriate location in the model. The model isadapted to reflect the configuration. Therefore, if the model has one ormore variables that reflect the configuration (objects in the otherslots, etc.), then those variables are defined as true or falsedepending on the configuration.

Then the predictive value of the object that is associated with thefirst slot of the first configuration is calculated 724 by executing themodel. Next, the calculated predictive value is written in the table'scell at the junction of the first configuration and the first slot and adecision is made 730 as to whether there are additional slots in theconfiguration. If so, method 700 returns to action 720 and starts theloop for the next slot (a second slot, etc.) in the first configuration.The loop may continue to the third slot and so on until there 730 are nomore slots in the configuration. Then, the predictive value of theconfiguration is calculated 732 by summing the predictive value of eachslot, for example. In other embodiments the predictive value of theconfiguration is calculated 732 by averaging the predictive value ofeach slot. etc.

At action 740, a determination is made as to whether there areadditional optional configurations in the table. If yes, method 700returns to action 710 and starts the loop for the next configuration (asecond configuration, etc) in the table. The loop may continue for thethird configuration and so on until there 740 are no more configurationsto be processed.

After calculating 740 the predictive value of all the possibleconfigurations of the web-page, an optimal configuration is selected742. Notably, as previously disclosed, the prediction value alsoreflects the benefit that the owner of CAS 200 will get when a surferresponds to the presented web-page via the selection of one of theoptional objects. The configuration with the highest predictive valuemay be defined as the preferred configuration. This preferredconfiguration, and its predictive value, may be stored 744 in the entryof AST 215 (FIG. 2) and delivered to MLFH 230 (FIG. 2) as the predictiveconfiguration. At this point, method 700 may be terminated 746. Method700 may be initiated again upon receiving a next delivered web-page thatis associated with POSM 250.

In an exemplary embodiment of CAS 2000 in which two or more KPIs areused. Method 700 can be implemented by using a ready predictive modelthat is associated to a KPI with the highest utility to the owner of theweb-site. Such embodiment may start using the predictive model in whichthe KPI reflects clicking on a certain object. Later it may switch to apredictive model that is associated with purchasing KPI, for example.

FIG. 8 illustrates a flowchart depicting relevant processes of anexemplary method 800 that may be used for monitoring the performance ofthe current predictive models of a certain web-page. Method 800 may beimplemented within the PMM 266 (FIG. 2), for example. In order toeliminate redundancy of information in the following paragraphs, actionsof method 800 that implemented in CAS 200 or CAS 2000, which aresimilar, are disclosed once, in relation with CAS 200. Base on readingthe description of method 800 for CAS 200, an ordinary skilled person inthe art is expected to understand how those similar actions areoperating in relation with CAS 2000. In some actions method 800 in whichCAS 2000 may operate in a different way or may need additionalinformation, this information will be added.

Method 800 may be initialized 802 by the manager module 264 (FIG. 2)during the “power on” process. After initiation, method 800 may run in aloop as long as CAS 200 is active. Method 800 may be used for monitoringthe results of the stored data at ELOG 242 (FIG. 2), which is assignedto the certain web-page being currently monitored, before transferringit to OHDB 248 (FIG. 2). After processing the records stored in ELOG242, an indication may be sent to OMM 246 of the PDC 240, which isassigned to the web-page that is currently monitored. The indication mayinform the OMM 246 that it may start the process of transferring theinformation from ELOG 242 to OHDB 248. In addition, the results of theevaluation may be transferred to the manager module 264 and/or PMB 262.After monitoring the results of one web-page, PMM 266 (FIG. 2) mayproceed to the next web-page and so on. In an alternate embodiment ofthe present disclosure, PMM 266 may execute a plurality of processes 800in parallel, one per each web-page. Method 800 can be implemented per aweb-page, per a portion of a web page or per set of web-pages. Thedecision is depending on the scope of the wish of the web-site owner.

After initiation, a timer Tm is reset 804 and the current mode ofoperation, which is defined by the manager module 264 (FIG. 2), isdetermined. If 810 the operating mode is a “learning” (training) mode,then method 800 may wait until the end of the learning period. If 810the operating mode is a “monitoring” mode, then method 800 may wait 814until the value of timer Tm is greater than a configurable value Tmm.Tmm may be in the range of a few minutes to a few tens of minutes, forexample. If 810 the operating mode is an “ongoing” mode, then method 800may wait 816 until the value of timer Tm is greater than a configurablevalue Tmo. Tmo may be in the range of a few tens of minutes to a fewhours, for example.

When timer Tm reaches the value of Tmm or Tmo, depending on theoperating mode, a web-page comparison table is allocated 820. Theweb-page comparison table may have two entries wherein one may beassigned to the average utility of the random surfer while the other maybe assigned to the average utility of the personalized surfer. Anexemplary method 800 which is implemented in a CAS 2000, for example,can have two or more web-page comparison tables. Each web-pagecomparison table can be associated with a KPI.

At action 836, the ELOG is scanned and the average utilities of therandom surfer and the personalized surfer are calculated. The resultsmay be written in the web-page comparison table at the appropriate entrythat is assigned to the relevant surfer.

The web-page comparison table is transferred 842 to the manager module264 (FIG. 2). Timer Tm is reset and indication is sent to OMM 246 (FIG.2) of the PDC 240, which is assigned to the relevant web-page. Inresponse, OMM 246 may start transferring the information from ELOG 242to OHDB 248 as previously disclosed. At such point, method 800 mayreturn to action 810 and, based on the current mode of operation, mayproceed per the methodologies previously disclosed. An exemplary method800 which is implemented in a CAS 2000, for example, can transfer thetwo or more web-page comparison tables. Each web-page comparison tablecan be associated with a KPI.

FIGS. 9A & 9B illustrate a flowchart depicting relevant processes of anexemplary method 900 that may be used for creating a new set of object'spredictive models, one per each optional-object that may be associatedwith a web-page that is served by CAS 200 (FIG. 2 a) or CAS 2000 (FIG. 2b). Method 900 may be implemented within the predictive model builder(PMB) 262 (FIG. 2), for example. In order to eliminate redundancy ofinformation in the following paragraphs, actions of method 900 thatimplemented in CAS 200 or CAS 2000, which are similar, are disclosedonce, in relation with CAS 200. Base on reading the description ofmethod 900 for CAS 200, an ordinary skilled person in the art isexpected to understand how those similar actions are operating inrelation with CAS 2000. In some actions method 900 in which CAS 2000 mayoperate in a different way or may need additional information, thisinformation will be added. In some embodiments method 900 can beimplemented on parallel processor simultaneously. Each processor can beassociated with a web page, and/or KPI, etc.

Method 900 may be initiated 902 by the manager module 264 (FIG. 2) eachtime a new set of predictive models is needed. During the initializationprocess 904, PMB 262 may allocate resources that may be needed forcalculation of the predictive models. Information, or memory pointers toinformation, needed while processing the predictive models may beretrieved. Required counters may be reset. Furthermore, a counter may beused as an index.

When the initial action 904 is completed, an external loop betweenactions 910 and 960 (FIG. 9 b) is initiated. Each cycle in the externalloop is associated with an optional-object (an alternative object) thatmay be associated with a web-page. At action 912, counter N isincremented by one, indicating the object's number that is currentlyhandled in the current cycle of the loop. The two historical DBs of theobject N, 249Ns (success) and 249Nf (failure) (FIG. 2 a), are processedand a representative weight per each ODB is calculated based on thedeleted portion that was associated to the ODB by OMM 246 (FIG. 2)during the transfer of records from ELOG 242 (FIG. 2) to the ODBs, aspreviously disclosed above in conjunction with action 516 (FIG. 5). Insome exemplary embodiments each ODB can be associated with two or morerepresentative weights. Each representative weight can be associatedwith one of the sub ODBs that compose the ODB. Yet in other embodiment arepresentative weight can be used. The representative weight can be theinverse sampling rate of each sub ODBs. In other embodiments three ormore criteria, can be used. Exemplary criteria can be few ranges ofrevenue in dollars: zero dollars, 1 to 10 dollars, 10 to 30 dollars, 30to hundreds dollars, etc.

In exemplary embodiments of process 900 that are implemented by CAS 2000the external loop between actions 910 and 960 (FIG. 9 b) may be repeatedalso for each KPI. Consequently a loop that is associated with KPInumber K and object number N handles ODB 249KNs ODB 249KNf (FIG. 2 b).In other exemplary embodiments of process 900, in which the predictivemodel per each KPI is implemented by another processor of PMB 262 in CAS2000, the external loop may be executed on the ODB 249KNs ODB 249KNfthat are associated with the same KPI as the processor.

The raw data from the success and failure ODB 249Ns & 249Nf areorganized 914 into an object's table. Each entry from the ODBs 249Ns &249Nf is copied into a line in the table. The lines are sorted by time,independent of any success indication. The newest record may be storedat the top of the object's table while the oldest record may be storedat the bottom of the object's table, but it should be understood thatchoice of storage organization should not limit the scope of thedisclosure.

Each record in the table, i.e. line, has a plurality of columns(fields). Each column may be associated with a factor of the record,which is stored in the record. Exemplary columns may be designated torepresent the weight of the record (the weight may reflect therepresentative weight of the ODB from which the record was copied), theresult, success or failure (not success), relevant URL keys that wereembedded within the associated information that was stored in therecord, attributes indication on the web-page and/or the object, etc.Additional columns may reflect the values of behavioral information,which is stored in the record. Other columns may represent informationsuch as counters and timers that were stored in the cookie associatedwith the request, indications of attributes associated with a surferthat were stored in the cookie, etc. A column can reflect the utility ofthe success to the website owner. The utility can be one of the KPIs. Insome embodiments in which the entire visit of a surfer is taken intoconsideration, then a column can be associated to the number of timesthe surfer, that is associated with the record, requested the relevantweb page during the same visit.

The associated information may be the receiving time of the request thatis written in the record, the URL that was associated with the request,information regarding the configuration of the web-page from which therequest was selected, etc. Each cell at the junction of a column and arow may store the URL key's value if it exists, missing key indicationif the record does not include the key, or a missing value indication ifthe record includes the key but no value, for example. Each cell in thejunction of a behavioral information factor and a line (record) maystore the value of a relevant counter, timer, etc. which are associatedwith the column.

The object's table may be searched 914 for irrelevant keys (fewinstances), for example. Irrelevant keys may be defined as keys thathave a small number of records (lines) in which the key has a value. Theminimum number of records may be a configurable value in the range of afew tens to a few thousands, depending on the volume of data stored inthe ODB 249, for example. The minimum number of records may be one ofthe properties that are stored in a set of properties, for example. Acolumn of irrelevant keys may be removed from the object's table. Theobject's table may be divided into two tables: a validation tableincluding the newer records and a training table with the older records.Usually, the training table includes more records than the validationtable. The validation table may be used later for determining thequality of the predictive models or the score of the model.

The training table is further processed 916 in order to calculate thepredictive model of the object. The type of the columns in the trainingtable may be defined. Defining the type may be executed by observing thekey in view of a plurality of syntax protocols and determining whetherthe value of the key complies with one or more of those protocols.

Next, the type is defined 916 based on the protocol. Exemplary protocolsmay be Internet protocols (IP), text protocols, time protocols, etc. Ifthe value of the key does not comply with any of the protocols, it maybe defined as “other.” Exemplary types may include IP, time, a list ofsearching words, text, numbers, currency, other, etc. Types of columnsthat are associated with behavioral information are known and mayinclude counters and timers, for example. After defining the type of thekeys, the type of the value of each key may be defined. Exemplary typesof values may be scale, ordinal, nominal, cyclic, etc.

In some exemplary embodiments, method 900 may further process 916 thetraining table in order to identify columns that may be divided into twoor more columns. For example, a time column may be divided into multiplecolumns representing days, hours, and minutes. An IP address may bedivided into four columns, etc. In other embodiments, an IP address canbe stored in a single column using 12 digits, padding with leading zerosand removing the points. Each sub-column (a portion of a predictivefactor) may be referred to as a predictive variable or a predictive key.

When the raw data is organized in the training table, method 900 startsconverting 916 key values that are not ordinal into ordinal values. Avalue of a text column (a certain string of text) may be converted intoan ordinal number that reflects its frequency (number of appearancesalong the column). A nominal number or a cyclic number may be convertedinto an ordinal number that reflects the influence of the nominal valueon the result of a record (success or failure). In other embodiment atext or nominal values can be represented in an ordinal way by sortingthe predictive power of each value. The predictive power can be definedas the ability to generate testable predictions.

At this point, the training table is ready to be further processed suchthat each column (a predictive variable/factor/key) is converted intoone or more bins and the training table converted overall into a binstable. In addition, a legend may be associated with the bin's table. Thelegend may define the predictive factor from which the bin wasprocessed, the interval that the bin includes and the predictiveefficiency of the bin. The bins table is further processed fordetermining a predictive model per each set of properties. A middle loopfrom action 920 to 950 (FIG. 9 b) may be initiated. Each cycle in theloop is associated with a set of properties. Each set of properties maydefine and comprise a set of parameters that may be used while preparinga predictive model.

An exemplary parameter may be relative aging weight. Other parametersmay define the minimum number of appearances of a certain key, a numberbelow which a key may be considered as irrelevant. Another parameter maydefine the minimal value of a predictive score that a predictive modelmay get in order to be used. Yet another parameter may define thehalf-life-time constant of a record, etc. In an exemplary embodiment ofmethod 900, which is implemented in CAS 2000, each KPI may have its ownset of properties. A KPI that reflects clicking rate may have differentparameters than KPI that reflects purchasing. For example the minimumnumber of appearances for clicking rate can be higher than the minimumnumber of appearance per purchasing, etc.

The value of M counter is incremented 922 by one indicating the IDnumber of the set of properties that is associated with the currentcycle of the loop. The appropriate set of properties is fetched andparsed and, according to its properties, an equivalent weight per eachline may be defined. The equivalent weight may reflect therepresentative weight which is due to the result of the record's (line)success or failure, age, the benefit that is created to the owner of CAS200 (FIG. 2) if the object is selected, etc. After defining theequivalent weight, method 900 proceeds to action 930 (FIG. 9B) andinitiates the internal loop between actions 930 to 940.

In some embodiments, the binning process is carried out with respect toa target KPI. The target KPI can be Boolean (0 or 1), discrete,continues or mixed as well. The binning process calculates the averageKPI within the bin and compares it to the average KPI outside the bin,while calculating the significance of the difference. This approach mayalso be used to split a column into two bins, generating the highestexplained variance, and further splitting of each bin. Each split of abin may have some constraints like certain allowed percentiles split andminimal size, etc.

Each cycle in the internal loop is associated with a column, i.e. apredictive variable. The range of the ordinal values written in thecolumn is divided 932 into one or more sub-intervals (bins) according tothe ability to predict the success. Dividing the range of the values ofthe column into bins may be executed in several methods. One exemplarymethod may divide the interval into a configurable number of equalintervals (units). The number of units (the resolution) may be a fewunits to a few tens of units, for example. The resolution may be one ofthe properties that are included in a set of properties, for example. Apredictive score per each interval unit, along the interval of thevalues of the current column, may be calculated by dividing the weightednumber of success records by the total weighted number of records, whichhave an ordinal value of the predictive variable (column), in thecurrent interval unit.

The one or more bins may be created 932 by grouping one or more adjacentinterval units into one bin, wherein the variance between the rates ofsuccess of the adjacent interval units is below a certain value, i.e. avariance threshold. The variance threshold may have a configurable valueand may be one of the properties that are stored in a set of properties,for example. After creating one or more bins for each predictivevariable (column), a rate of success is calculated for the bin. The rateof success is calculated as the weighted number of success recordsdivided by the weighted total number of records. Information on the bins(information on its predictive variable, the bin's interval, etc.) alongwith associated prediction scores is stored in a bins legend. Then, adetermination is made 940 as to whether the training table includes morecolumns (predictive variables). If 940 yes, method 900 returns to action930 and starts a new cycle in the internal loop for dividing the nextpredictive variable (a next column, in the training table) into one ormore bins. In embodiments that target a non Boolean KPI, the average KPIcan replace the rate of success.

If 940 there are no more columns, then a bins table is created 942. Thebins table may have a column per each bin and the same number of linesas in the training table. In each cell, at the junction of a line(record) and a column (bin), a binary value, true or false, may bewritten depending on the value of the relevant predictive variable. Thevalue of the relevant predictive variable is in the range of values thatis associated with bin.

In another exemplary embodiment of method 900, action 942 may include avalidation process. An exemplary validation process may repeat the loopfrom action 930 to 940 on the records that are stored in the validationtable. If the bins that were created by processing the validation tableare similar to the bins that were created by processing the trainingtable, then the bins may be considered as valid and method 900 mayproceed to action 944. If not, one or more properties in the current setof properties may be slightly modified. For example, the resolution ofthe range of a certain column may be reduced. Then, the loop from action930 to 940 may be repeated twice with the modified set of parameters.During the first repetition a second set of bins is calculated using therecords stored in the training table. During the second repetition, thesecond set is validated by using the validation table. The validationprocess may have one or more cycles. In some exemplary embodiments thebins validation table may have other records than the validation tablethat is used for validating the predictive models.

Yet another exemplary embodiment of method 900 may respond to non validsets of bins by marking the set of properties as a problematic one andmay jump to action 950, skipping the stage of calculating a predictivemodel according to the problematic set of parameters. In yet anotherembodiment, a set of bins may be ignored entirely.

The bins table and the bins legend is transferred 944 toward apredictive model engine. The predictive model engine may implement astatistical algorithm over the bins table in order to calculate thepredictive model. The statistical algorithm can be such as, but notlimited to, logistic regression, linear regression, decision treeanalysis, etc. The calculated predictive model that was created isstored as a prediction model (N;M). The N stands for the optional-objectfor which the model was calculated and the M stands for the set ofproperties that was used for calculating the model.

At action 950, a determination is made as to whether there are more setsof properties. If yes, method 900 returns to action 920 (FIG. 9 a) andstarts a new cycle for creating an additional predictive model, for theobject N, based on the next set of properties (M+1). If 950 there are nomore sets of properties, then an object's predictive model N iscalculated 952 as an equivalent model of the M models that werecalculated and stored in action 944 as it is disclosed below.

An exemplary predictive model (N:M) may include one or more constants,one or more predictive variables that are derived from associatedinformation, each having an associated coefficient, one or morepredictive variables that are derived from behavioral information, eachhaving an associated coefficient, one or more web-page configurationvariables that reflect objects in the other slots, each having anassociated coefficient.

In some embodiments, additional predictive variables may be used. Forexample, one or more predictive variables that are derived from web-pageattributes, surfer's attributes, or object attributes may be included.Exemplary constants may be a result of the regression process (anarithmetic constant), for example. Another constant may reflect thebenefit of selecting the object M. Exemplary predictive variables thatare derived from associated information may be the day, the browsertype, etc. An exemplary predictive variable that is derived from thebehavioral information stored in the cookie may be the number of visitsto a certain page, etc. An exemplary web-page configuration variable maybe object X in slot Y, for example.

An exemplary calculation of a Boolean KPI (true, fails) predictive valueof an object may be executed based on the following formula:

$P = \frac{1}{1 + ^{- {({{Sum}\mspace{14mu} {of}\mspace{14mu} {relevant}\mspace{14mu} {coefficients}})}}}$

The relevant coefficients are the coefficients that are associated withvariables that are true for a received request.

An exemplary method 900 may execute 952 the M models of theoptional-object N on the records that are stored in the validationtable. The M models may be executed on each record in the validationtable that includes the object N in a web-page configuration that wassent as a response to the request that initiated the record. Afterperforming the M models on the validation table, a predictive fitnessscore is calculated per each model. In an alternate embodiment of thepresent disclosure, the records that are stored in the validation tableplus the records that are stored in the training table may be used inaction 952.

PMB 262 may select 952 a group of calculated predictive models out ofthe M models that have the best scores. Then, the object N predictivemodel may be calculated as a representative model of the group of thebest models. Calculating the representative model may be executed bycalculating an average value per each coefficient. The average may beweighted by the predictive score of the selected best models, forexample. The representative model may be stored as “object N ready to beused predictive model”. A bin that does not exist in a model isconsidered to have a coefficient of zero. In an embodiment, theequivalent model can be generated by merging the M models using all thedata, the training and the validation. The equivalent model will becreated based on the previously calculated fitness scores. In thisdiscloser the terms: “equivalent model ” and “representative model” canbe used interchangeably.

At action 960, a decision is made whether there are additionaloptional-objects that may be associated with the relevant web-page. Ifyes, method 900 returns to action 910 and start the external loop forcalculating a prediction model for a next object (M+1). If there are nomore objects, Manager module 264 (FIG. 2) may be informed that a set ofobject's predictive models for that web-page are ready 962 and method900 may be terminated 964. Method 900 may be initiated again forhandling a second web-page. In an alternate embodiment of the presentdisclosure, several processes 900 may be executed in parallel, one pereach web-page. In an exemplary embodiment of method 900, which isimplemented in CAS 2000, method 900 may be repeated from step 904 pereach KPI in order to generate a predictive model per each KPI. Yet inother embodiments several processes 900 may be executed in parallel, oneper each KPI.

Usually each KPI has a different distribution, therefore predictivemodels of the same page that are based on different KPI may be ready ata different time. It is expected that the predictive model, which isbased on clicking as a KPI, will be valid first because the clickingoccurs more frequently than purchasing, for example. Consequently themanager model 264 (FIG. 2 a&b) will load to the relevant POSM 250 thefirst ready to use predictive model, which is base on clicking rate.When a predictive model, which is based on the purchasing rate will bevalid than the manager 264 may replace the current active predictivemodel, which is based on clicking rate, with the new predictive modelthat is based on purchasing rate. Switching from one predictive model toanother predictive model improves the benefit of the web-site owner. Theweb-site owner is more interesting in increasing the purchasing ratethat the clicking rate.

It is to be understood that the above description is intended to beillustrative, and not restrictive. The above-described apparatus,systems, and methods may be varied in many ways, including, changing theorder of steps, and the exact implementation used. The describedembodiments include different features, not all of which are required inall embodiments of the present disclosure. Moreover, some embodiments ofthe present disclosure use only some of the features or possiblecombinations of the features. Different combinations of features notedin the described embodiments will occur to a person skilled in the art.Furthermore, some embodiments of the present disclosure may beimplemented by combination of features and elements that have beendescribed in association to different exemplary embodiments along thediscloser. The scope of the invention is limited only by the followingclaims and equivalents thereof.

1. A method for creating a predictive model to select an object from agroup of objects that can be associated with a requested web-page,wherein a configuration of the requested web-page defines a subgroup ofone or more selected objects from the group of objects, the methodcomprising: collecting information from a plurality of requests to theweb-page that were responded by a web-page that included the object andcollecting information on the results of presenting the object; defininga plurality of predictive factors to be extracted from the collectedinformation; extracting, per each collected request, a value per eachpredictive factor; defining one or more bins, wherein each bin isassociated with a range of values of one or more predictive factors, andwherein the bins are defined based on their explained variance; creatinga bins table by defining per each collected request a value per eachbin; and processing the bins table with a predictive model engineconfigured to generate a predictive model based on data stored in thebins table.
 2. The method of claim 1, wherein the object is an image. 3.The method of claim 1, wherein the action of extracting, per eachcollected request, a value per each predictive factor further comprisingconverting non-ordinal values of predictive factor into ordinal valuesof predictive factor.
 4. The method of claim 3, wherein the action ofconverting non-ordinal values of predictive factor into ordinal valuesis based on the distribution of the values.
 5. The method of claim 1,wherein the action of converting the information into a plurality ofpredictive factors further comprises converting the configuration ofweb-pages that were transmitted in response to requests into one or morepredictive factors.
 6. The method of claim 1, wherein the predictionalgorithm used by the predictive model engine to generate a predictivemodel is selected from a group consisting of: logistic regression,linear regression and decision tree analysis.
 7. The method of claim 1,wherein the collected information is embedded with the requests to theweb pages.
 8. The method of claim 7, wherein one or more cookies areembedded with the request and the information is collected also from theone or more cookies.
 9. The method of claim 1, wherein the informationcomprises nominal values.
 10. The method of claim 9, wherein theinformation represents one or more of: a surfer's IP address; a receipttime for a request; a communication application used by a surfer; or aURL that is associated with a request.
 11. The method of claim 1,wherein the collected information further comprising a surfer's pastbehavior.
 12. The method of claim 11, wherein the collected informationis time dependent.
 13. The method of claim 11, wherein the collectedinformation represents one or more of: time of one or more previousvisits by a surfer to a domain that includes a requested web-page; timethat a surfer requested a certain web-page; the number of visits of asurfer in a domain; or the number of selections by a surfer of a certainobject.
 14. The method of claim 1, wherein the actions of defining oneor more bins and processing the bins table with a predictive modelengine configured to generate a predictive model include the use ofproperties' set selected from a group of properties' sets.
 15. Themethod of claim 14, wherein the actions of defining one or more bins andprocessing the bins table with a predictive model engine configured togenerate a predictive model are repeated per each properties' set in thegroup of properties' sets for creating a group of predictive models. 16.The method of claim 15, wherein an equivalent predictive model isgenerated from the group of predictive models.
 17. The method of claim1, further comprising: validating the generated predictive model. 18.The method of claim 1, further comprising: validating the created binstable.
 19. The method of claim 1, wherein collecting information furthercomprising collecting information relating to one or morekey-performance indicators (KPI).
 20. The method of claim 19, whereinone of the KPI of an object is selecting the object.
 21. The method ofclaim 19, wherein one of the KPI reflects revenue.
 22. The method ofclaim 19, wherein the actions of defining one or more bins andprocessing the bins table with a predictive model engine configured togenerate a predictive model are repeated per each KPI for creating apredictive model per each KPI.
 23. The method of claim 22, furthercomprising: validating a first generated predictive model based on afirst KPI; and utilizing the validated first predictive model in orderto select objects to be presented in the requested web page.
 24. Themethod of claim 23, further comprising validating a second generatedpredictive model based on a second KPI, wherein the second KPI is morevaluable than the first KPI; and utilizing the validated secondpredictive model in order to select objects to be presented in thefollowing requested for the web page instead of utilizing the validatedfirst predictive model.
 25. The method of claim 1, wherein the action ofcollecting information from a plurality of requests to the web-pagefurther comprises information related to a visit of a surfer in a website that is related to the web page.
 26. The method of claim 25,wherein the created predictive model reflects the number of times thesurfer requested the web page during the surfer's visit.
 27. Anon-transitory storage medium readable by a processor and storinginstructions for execution by the processor, when executed by theprocessor, performs a method for creating a predictive model to selectan object from a group of objects that can be associated with arequested web-page, wherein a configuration of the requested web-pagedefines a subgroup of one or more selected objects from the group ofobjects, the method comprising: collecting information from a pluralityof requests to the web-page that were responded by a web-page thatincluded the object and collecting information on the results ofpresenting the object; defining a plurality of predictive factors to beextracted from the collected information; extracting, per each collectedrequest, a value per each predictive factor; defining one or more bins,wherein each bin is associated with a range of values of one or morepredictive factors, and wherein the bins are defined based on theirexplained variance; creating a bins table by defining per each collectedrequest a value per each bin; and processing the bins table with apredictive model engine configured to generate a predictive model basedon data stored in the bins table.
 28. The non-transitory storage mediumof claim 27, wherein the collected information is embedded with therequests to the web pages.
 29. The non-transitory storage medium ofclaim 27, wherein one or more cookies are embedded with the request andthe information is collected also from the one or more cookies.
 30. Thenon-transitory storage medium of claim 27, wherein the instructions ofdefining one or more bins and processing the bins table with apredictive model engine configured to generate a predictive modelinclude the use of properties' set selected from a group of properties'sets.
 31. The non-transitory storage medium of claim 30, wherein theinstruction of defining one or more bins and processing the bins tablewith a predictive model engine configured to generate a predictive modelare repeated per each properties' set in the group of properties' setsfor creating a group of predictive models.
 32. The non-transitorystorage medium of claim 31, wherein an equivalent predictive model isgenerated from the group of predictive models.
 33. The non-transitorystorage medium of claim 27, wherein collecting information furthercomprising collecting information relating to one or morekey-performance indicators (KPI).
 34. The non-transitory storage mediumof claim 33, wherein one of the KPI of an object is selecting theobject.
 34. The non-transitory storage medium of claim 33, wherein oneof the KPI of an object reflects revenue.
 35. The non-transitory storagemedium of claim 33, wherein the instructions of defining one or morebins and processing the bins table with a predictive model engineconfigured to generate a predictive model are repeated per each KPI forcreating a predictive model per each KPI.